Forex, Stock Market, and The Economy Megathread

[Deleted member 6403]

While most forex is traded by large institutions, banks and corporations, there are a great many retail foreign exchange traders.

These are traders that will trade on their own accounts and buy / sell forex in order to target a specific return. This has increased dramatically recently with internet usage and online trading platforms.[2]

In the moral context this practice is to be avoided as personal behavior and also considering potential damage to the real economy.

Risk aversion​

Risk aversion is a kind of trading behavior exhibited by the foreign exchange market when a potentially adverse event happens that may affect market conditions.

This behavior is caused when risk averse traders liquidate their positions in risky assets and shift the funds to less risky assets due to uncertainty.[84]


In the context of the foreign exchange market, traders liquidate their positions in various currencies to take up positions in safe-haven currencies, such as the US dollar.[85]

Sometimes, the choice of a safe haven currency is more of a choice based on prevailing sentiments rather than one of economic statistics.

An example would be the financial crisis of 2008. The value of equities across the world fell while the US dollar strengthened (see Fig.1). This happened despite the strong focus of the crisis in the US.[86]

Speculation​

Controversy about currency speculators and their effect on currency devaluations and national economies recurs regularly. Economists, such as Milton Friedman, have argued that speculators ultimately are a stabilizing influence on the market, and that stabilizing speculation performs the important function of providing a market for hedgers and transferring risk from those people who don't wish to bear it, to those who do.[79]

Other economists, such as Joseph Stiglitz, consider this argument to be based more on politics and a free market philosophy than on economics.[80]Large hedge funds and other well capitalized "position traders" are the main professional speculators. According to some economists, individual traders could act as "noise
traders" and have a more destabilizing role than larger and better informed actors.[81]

Currency speculation is considered a highly suspect activity in many countries.[where?] While investment in traditional financial instruments like bonds or stocks often is considered to contribute positively to economic growth by providing capital, currency speculation does not; according to this view, it is simply gambling that often interferes with economic policy. For example, in 1992, currency speculation forced Sweden's central bank, the Riksbank, to raise interest rates for a few days to 500% per annum, and later to devalue the krona.[82] Mahathir Mohamad, one of the former Prime Ministers of Malaysia, is one well-known proponent of this view. He blamed the devaluation of the Malaysian ringgit in 1997 on George Soros and other speculators.

Gregory Millman reports on an opposing view, comparing speculators to "vigilantes" who simply help "enforce" international agreements and anticipate the effects of basic economic "laws" in order to profit.[83] In this view, countries may develop unsustainable economic bubbles or otherwise mishandle their national economies, and foreign exchange speculators made the inevitable collapse happen sooner. A relatively quick collapse might even be preferable to continued economic mishandling, followed by an eventual, larger, collapse. Mahathir Mohamad and other critics of speculation are viewed as trying to deflect the blame from themselves for having caused the unsustainable economic conditions.

Noise trader​

A noise trader is a stock trader whose decisions to buy or sell are based on "factors they believe to be helpful but in reality will give them no better returns than random choices".[1] These factors may include hype or rumor, which noise traders believe to be reliable signals of future returns, but which are actually forms of economic noise that cannot be used to accurately predict the future value of a stock.[2]

Noise traders do not trade randomly; their decisions are systematic.[1] However, their trading decisions are not based on professional advice or a business's fundamentals,[2] and the purported signals used by noise traders are more unreliable than those used by technical analysts[citation needed].[1] Therefore, returns on their trading decisions are expected to be no better than random choices.[1]

Noise traders often act irrationally: they tend to be emotion-driven, impulsive, reactive, and herd-like.[3] The presence of noise traders in financial markets can cause prices and risk levels to diverge from expected levels even if all other traders are rational.[4]

Carry (investment)​

The currency carry trade is an uncovered interest arbitrage. The term carry trade, without further modification, refers to currency carry trade: investors borrow low-yielding currencies and lend (invest in) high-yielding currencies. It is thought to correlate with global financial and exchange rate stability and retracts in use during global liquidity shortages,[3] but the carry trade is often blamed for rapid currency value collapse and appreciation.

A risk in carry trading is that foreign exchange rates may change in such a way that the investor would have to pay back more expensive currency with less valuable currency. In theory, according to uncovered interest rate parity, carry trades should not yield a predictable profit because the difference in interest rates between two countries should equal the rate at which investors expect the low-interest-rate currency to rise against the high-interest-rate one. However, carry trades weaken the currency that is borrowed, because investors sell the borrowed money by converting it to other currencies.

By early year 2007, it was estimated that some US$1 trillion may have been staked on the yen carry trade.[4] Since the mid-1990s, the Bank of Japan has set Japanese interest rates at very low levels making it profitable to borrow Japanese yen to fund activities in other currencies.[5] These activities include subprime lending in the USA, and funding of emerging markets, especially BRIC countries and resource rich countries. The trade largely collapsed in 2008 particularly in regard to the yen.

The European Central Bank extended its quantitative easing programme in December 2015. Accommodative ECB monetary policy made low-yielding EUR an often used funding currency for investment in risk assets. The EUR was gaining in times of market stress (such as falls in China stocks in January 2016), although it was not a traditional safe-haven currency.[6]

Most research on carry trade profitability was done using a large sample size of currencies.[7] However, small retail traders have access to limited currency pairs, which are mostly composed of the major G20 currencies, and experience reductions in yields after factoring in various costs and spreads.[8]

Arbitrage​

In economics and finance, arbitrage (/ˈɑːrbɪtrɑːʒ/, UK also /-trɪdʒ/) is the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices at which the unit is traded. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state; in simple terms, it is the possibility of a risk-free profit after transaction costs. For example, an arbitrage opportunity is present when there is the possibility to instantaneously buy something for a low price and sell it for a higher price.

In principle and in academic use, an arbitrage is risk-free; in common use, as in statistical arbitrage, it may refer to expected profit, though losses may occur, and in practice, there are always risks in arbitrage, some minor (such as fluctuation of prices decreasing profit margins), some major (such as devaluation of a currency or derivative). In academic use, an arbitrage involves taking advantage of differences in price of a single asset or identical cash-flows; in common use, it is also used to refer to differences between similar assets (relative value or convergence trades), as in merger arbitrage.

The term is mainly applied to trading in financial instruments, such as bonds, stocks, derivatives, commodities, and currencies. People who engage in arbitrage are called arbitrageurs (/ˌɑːrbɪtrɑːˈʒɜːr/).

Arbitrage has the effect of causing prices of the same or very similar assets in different markets to converge.

Conditions for arbitrage​

Arbitrage is possible when one of three conditions is met:

1. The same asset does not trade at the same price on all markets ("the law of one price").
2. Two assets with identical cash flows do not trade at the same price.
3. An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or the asset has significant costs of storage; so this condition holds true for something like grain but not for securities).

Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time. The transactions must occur simultaneously to avoid exposure to market risk, or the risk that prices may change on one market before both transactions are complete. In practical terms, this is generally possible only with securities and financial products that can be traded electronically, and even then, when each leg of the trade is executed, the prices in the market may have moved. Missing one of the legs of the trade (and subsequently having to trade it soon after at a worse price) is called 'execution risk' or more specifically 'leg risk'.[note 1]

In the simplest example, any good sold in one market should sell for the same price in another. Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a higher price. This type of price arbitrage is the most common, but this simple example ignores the cost of transport, storage, risk, and other factors. "True" arbitrage requires that there is no market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other.

See rational pricing, particularly § arbitrage mechanics, for further discussion.

Mathematically it is defined as follows:

Market psychology​

Market psychology and trader perceptions influence the foreign exchange market in a variety of ways:

• Flights to quality: Unsettling international events can lead to a "flight-to-quality", a type of capital flight whereby investors move their assets to a perceived "safe haven". There will be a greater demand, thus a higher price, for currencies perceived as stronger over their relatively weaker counterparts. The US dollar, Swiss franc and gold have been traditional safe havens during times of political or economic uncertainty.[73]
• Long-term trends: Currency markets often move in visible long-term trends. Although currencies do not have an annual growing season like physical commodities, business cycles do make themselves felt. Cycle analysis looks at longer-term price trends that may rise from economic or political trends.[74]
• "Buy the rumor, sell the fact": This market truism can apply to many currency situations. It is the tendency for the price of a currency to reflect the impact of a particular action before it occurs and, when the anticipated event comes to pass, react in exactly the opposite direction. This may also be referred to as a market being "oversold" or "overbought".[75] To buy the rumor or sell the fact can also be an example of the cognitive bias known as anchoring, when investors focus too much on the relevance of outside events to currency prices.
• Economic numbers: While economic numbers can certainly reflect economic policy, some reports and numbers take on a talisman-like effect: the number itself becomes important to market psychology and may have an immediate impact on short-term market moves. "What to watch" can change over time. In recent years, for example, money supply, employment, trade balance figures and inflation numbers have all taken turns in the spotlight.
• Technical trading considerations: As in other markets, the accumulated price movements in a currency pair such as EUR/USD can form apparent patterns that traders may attempt to use. Many traders study price charts in order to identify such patterns.[76]

Technical analysis​

Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power.[46] Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.[6] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.[47] A Federal Reserve working paper[7] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions", although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".

Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50 percent."[48]

An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems in 1999;[49] the sample covered by Brock et al. was robust to data snooping.

Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices."[19] Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.[50]

In a paper published in the Journal of Finance, Dr. Andrew W. Lo, director MIT Laboratory for Financial Engineering, working with Harry Mamaysky and Jiang Wang found that:

Technical analysis, also known as "charting", has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis – the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution – conditioned on specific technical indicators such as head-and-shoulders or double-bottoms – we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.[8]

In that same paper Dr. Lo wrote that "several academic studies suggest that ... technical analysis may well be an effective means for extracting useful information from market prices."[8] Some techniques such as Drummond Geometry attempt to overcome the past data bias by projecting support and resistance levels from differing time frames into the near-term future and combining that with reversion to the mean techniques.[51]

Efficient-market hypothesis[edit]​

The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse."[52]

However, because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices.[53] They also point to research in the field of behavioral finance, specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes.[54] Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis:

By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies.... cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work.[53]

EMH advocates reply that while individual market participants do not always act rationally (or have complete information), their aggregate decisions balance each other, resulting in a rational outcome (optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium).[55] Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.[55]

Random walk hypothesis[edit]​

The random walk hypothesis may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements (but not necessarily other public information). In his book A Random Walk Down Wall Street, Princeton economist Burton Malkiel said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future."[56] Malkiel has stated that while momentum may explain some stock price movements, there is not enough momentum to make excess profits. Malkiel has compared technical analysis to "astrology".[57]

In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a 1999 response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability[58] that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH, which is an entirely separate concept from RWH. In a 2000 paper, Andrew Lo back-analyzed data from the U.S. from 1962 to 1996 and found that "several technical indicators do provide incremental information and may have some practical value".[8] Burton Malkiel dismissed the irregularities mentioned by Lo and McKinlay as being too small to profit from.[57]

Technicians say[who?] that the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves.[28][59] Some signal processing researchers negate the random walk hypothesis that stock market prices resemble Wiener processes, because the statistical moments of such processes and real stock data vary significantly with respect to window size and similarity measure.[60] They argue that feature transformations used for the description of audio and biosignals can also be used to predict stock market prices successfully which would contradict the random walk hypothesis.

The random walk index (RWI) is a technical indicator that attempts to determine if a stock's price movement is random in nature or a result of a statistically significant trend. The random walk index attempts to determine when the market is in a strong uptrend or downtrend by measuring price ranges over N and how it differs from what would be expected by a random walk (randomly going up or down). The greater the range suggests a stronger trend.[61]

Applying Kahneman and Tversky's prospect theory to price movements, Paul V. Azzopardi provided a possible explanation why fear makes prices fall sharply while greed pushes up prices gradually.[62] This commonly observed behaviour of securities prices is sharply at odds with random walk. By gauging greed and fear in the market,[63] investors can better formulate long and short portfolio stances.

Fear & Greed Index - Investor Sentiment - CNNMoney

Fear & Greed is CNNMoney's investor sentiment tool that comprises of 7 markets indicators.

money.cnn.com

Scientific technical analysis​

Caginalp and Balenovich in 1994[64] used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions. Some of the patterns such as a triangle continuation or reversal pattern can be generated with the assumption of two distinct groups of investors with different assessments of valuation. The major assumptions of the models are that the finiteness of assets and the use of trend as well as valuation in decision making. Many of the patterns follow as mathematically logical consequences of these assumptions.

One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing.

Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend. Caginalp and Laurent[65] were the first to perform a successful large scale test of patterns. A mathematically precise set of criteria were tested by first using a definition of a short-term trend by smoothing the data and allowing for one deviation in the smoothed trend. They then considered eight major three-day candlestick reversal patterns in a non-parametric manner and defined the patterns as a set of inequalities. The results were positive with an overwhelming statistical confidence for each of the patterns using the data set of all S&P 500 stocks daily for the five-year period 1992–1996.

Among the most basic ideas of conventional technical analysis is that a trend, once established, tends to continue. However, testing for this trend has often led researchers to conclude that stocks are a random walk. One study, performed by Poterba and Summers,[66] found a small trend effect that was too small to be of trading value. As Fisher Black noted,[67] "noise" in trading price data makes it difficult to test hypotheses.

One method for avoiding this noise was discovered in 1995 by Caginalp and Constantine[68] who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation. A closed-end fund (unlike an open-end fund) trades independently of its net asset value and its shares cannot be redeemed, but only traded among investors as any other stock on the exchanges. In this study, the authors found that the best estimate of tomorrow's price is not yesterday's price (as the efficient-market hypothesis would indicate), nor is it the pure momentum price (namely, the same relative price change from yesterday to today continues from today to tomorrow). But rather it is almost exactly halfway between the two.

Starting from the characterization of the past time evolution of market prices in terms of price velocity and price acceleration, an attempt towards a general framework for technical analysis has been developed, with the goal of establishing a principled classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time translational invariant properties.[69] The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. Using a renormalisation group approach, the probabilistic based scenario approach exhibits statistically significant predictive power in essentially all tested market phases.

A survey of modern studies by Park and Irwin[70] showed that most found a positive result from technical analysis.

In 2011, Caginalp and DeSantis[71] have used large data sets of closed-end funds, where comparison with valuation is possible, in order to determine quantitatively whether key aspects of technical analysis such as trend and resistance have scientific validity. Using data sets of over 100,000 points they demonstrate that trend has an effect that is at least half as important as valuation. The effects of volume and volatility, which are smaller, are also evident and statistically significant. An important aspect of their work involves the nonlinear effect of trend. Positive trends that occur within approximately 3.7 standard deviations have a positive effect. For stronger uptrends, there is a negative effect on returns, suggesting that profit taking occurs as the magnitude of the uptrend increases. For downtrends the situation is similar except that the "buying on dips" does not take place until the downtrend is a 4.6 standard deviation event. These methods can be used to examine investor behavior and compare the underlying strategies among different asset classes.

In 2013, Kim Man Lui and T Chong pointed out that the past findings on technical analysis mostly reported the profitability of specific trading rules for a given set of historical data. These past studies had not taken the human trader into consideration as no real-world trader would mechanically adopt signals from any technical analysis method. Therefore, to unveil the truth of technical analysis, we should get back to understand the performance between experienced and novice traders. If the market really walks randomly, there will be no difference between these two kinds of traders. However, it is found by experiment that traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.[72]

Conclusion
There is much data available in nance literature, from banks and markets, about costs, stocks and currency exchange rates or option values, on futures, discount and interest rates, ::: the more so with the advent of the web. There are many levels of observation: individual income(s), individual expenses, checking accounts and savings, number of public or private accounts, volumes, debts and credits, tellers, dealers, bank outlets, businesses, governments, so many, that one is immediately tempted to play statistics. Fortunately, physicists have learned to develop intuition rst during their studies and when doing some research. They can build models. However, these cannot be realistic if they do not reproduce experimental data. The above techniques have been described in order to give some insight into a few which can lead to ne analysis in order to obtain reliable values of characteristic parameters for physicists, those pertaining to the realm of power-law exponents. Later, physicists knowing the limits of their models and of their understanding will be honestly questioning at all levels their ndings. At this time it is already possible to bring to Economy and in particular to Finance Theory a paraphernalia of tricks, theoretical or experimental ones. Words like coherence eects, correlation lengths, relaxation times, many body interactions, grand canonical ensembles, spins, phase transitions, critical exponents, mean eld approximations, renormalization group, cellular automata, organized criticality,.. are available for wrapping our gifts to business people. Moreover the techniques can be already implemented for personal investment, either with winning strategy or simply playing games with pocket money. Predictability models will not be found in a crystal ball but rather from models derived from spin glasses or sandpile avalanches and after using time-series analysis techniques as the ones here above

Quantifying the randomness of the forex market​

Currency markets are international networks of participants opened all day during weekdays without a supervisory entity. The precise value of an exchange pair is determined by the decisions of the central banks and the behavior of the speculators, whose actions can be determined on the spot or be related to previous decisions. All those decisions affect the complexity and predictability of the system, which are quantitatively analyzed in this paper. For this purpose, we compare the randomness of the most traded currencies in the forex market using the Pincus Index. We extend the development of this methodology to include multidimensionality in the embedding dimension, to capture the influence of the past in current decisions and to analyze different frequencies within the data with a multiscale approach. We show that, in general, the forex market is more predictable using one hour ticks than using daily data for the six major pairs, and present evidence suggesting that the variance is easier to predict for longer time frames.

The most traded pairs of currencies in the world are called the 6 Majors. They constitute the largest share of the foreign exchange market, about 85%, and therefore they exhibit high market liquidity. The Majors are: EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CHF and USD/CAD.

Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network​

The foreign exchange (FOREX) market is one of the biggest financial markets in the world. More than 5.1 trillion dollars are traded each day in the FOREX market by banks, retail traders, corporations, and individuals. Due to complex, volatile, and high fluctuation, it is quite difficult to guess the price ahead of the actual time. Traders and investors continuously look for new methods to outperform the market and to earn a higher profit. Therefore, researchers around the world are continuously coming up with new forecasting models to successfully predict the nature of this unsettled market. This paper presents a new model that combines two powerful neural networks used for time series prediction: Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM), for predicting the future closing prices of FOREX currencies.

The first layer of our proposed model is the GRU layer with 20 hidden neurons and the second layer is the LSTM layer with 256 hidden neurons. We have applied our model on four major currency pairs: EUR/USD, GBP/USD, USD/CAD, and USD/CHF. The prediction is done for 10 minutes timeframe using the data from January 1, 2017 to December 31, 2018, and 30 minutes timeframe using the data from January 1, 2019 to June 30, 2020 as a proof-of-concept. The performance of the model is validated using MSE, RMSE, MAE, and R2score. Moreover, we have compared the performance of our model against a standalone LSTM model, a standalone GRU model and simple moving average (SMA) based statistical model where the proposed hybrid GRU-LSTM model outperforms all models for 10-mins timeframe and for 30-mins timeframe provides the best result for GBP/USD and USD/CAD currency pairs in terms of MSE, RMSE, and MAE performance metrics. But in terms of R2score, our system outperforms all compared models and thus proves itself as the least risky model among all.

Image processing meets time series analysis: Predicting Forex profitable technical pattern positions​

Using technical price patterns is one of the well-known techniques for predicting future trends in financial markets. Some of these patterns are profitable under certain conditions and some might be non-profitable based on the target market situation and spread. This paper aims to propose a model that works along with the moving average crossover technical pattern. The outputs of the technical price pattern, which are long or short signals, are given as input to the proposed model to predict its profitability. We use a joint model that benefits from two different types of intelligent processing techniques, namely image processing which is applied to candlesticks extracted from price history, and time series analysis which is applied to the numerical features. For the former process, Convolutional Neural Network (CNN) is used and for the latter process, CNN with Long Short-Term Memory (LSTM) is used for the prediction. The proposed model is applied to the data from EUR/USD pairs. The tests were performed for spread values of 0.5, 1, 1.5, and 2. We show that the hybrid model achieves superior results compared to the individual ones, Relative Strength Index (RSI) and Bollinger Bands (BB) technical analysis patterns, as well as two state-of-the-art price prediction models based on CNN-Bidirectional LSTM (BiLSTM) and Phase-State Reconstruction (PSR) with LSTM.

Price action trading​

There is no evidence that these explanations are correct even if the price action trader who makes such statements is profitable and appears to be correct.[10] Since the disappearance of most pit-based financial exchanges, the financial markets have become anonymous, buyers do not meet sellers, and so the feasibility of verifying any proposed explanation for the other market participants' actions during the occurrence of a particular price action pattern is exceedingly small. Also, price action analysis can be subject to survivorship bias for failed traders do not gain visibility. Hence, for these reasons, the explanations should only be viewed as subjective rationalizations and may quite possibly be wrong, but at any point in time they offer the only available logical analysis with which the price action trader can work.

The implementation of price action analysis is difficult, requiring the gaining of experience under live market conditions. There is every reason to assume that the percentage of price action speculators who fail, give up or lose their trading capital will be similar to the percentage failure rate across all fields of speculation. According to widespread folklore / urban myth, this is 90%, although analysis of data from US forex brokers' regulatory disclosures since 2010 puts the figure for failed accounts at around 75% and suggests this is typical.[11]

Some skeptical authors[12] dismiss the financial success of individuals using technical analysis such as price action and state that the occurrence of individuals who appear to be able to profit in the markets can be attributed solely to the Survivorship bias.

 

[Deleted member 6573]

harvard business schools me

 

[Tyronecell]

Not a single word

 

[Deleted member 6403]

Behavioural economics concepts​

Conventional economics assumes that all people are both rational and selfish. In practice, this is often not the case, which leads to the failure of traditional models. Behavioural economics studies the biases, tendencies and heuristics that affect the decisions that people make to improve, tweak or overhaul traditional economic theory. It aids in determining whether people make good or bad choices and whether they could be helped to make better choices. It can be applied both before and after a decision is made.

Search heuristics[edit]​

Before a decision is made, there needs to be a minimum of two options. Behavioural economics employs search heuristics to explain how a person may evaluate their options. Search heuristics is a school of thought that suggests that when making a choice, it is costly to gain information about options and that methods exist to maximise the utility that one might get from searching for information. While each heuristic is not wholistic in its explanation of the search process alone, a combination of these heuristics may be used in the decision making process. There are three primary search heuristics.

Satisficing

Satisficing is the idea that there is some minimum requirement from the search and once that has been met, stop searching. Following the satisficing heuristic a person may not necessarily acquire the most optimal product (i.e. the one that would grant them the most utility), but would find one that is "good enough". This heuristic may be problematic if the aspiration level is set at such a level that no products exist that could meet the requirements.

Directed cognition

Directed cognition is a search heuristic in which a person treats each opportunity to research information as their last. Rather than a contingent plan that indicates what will be done based on the results of each search, directed cognition considers only if one more search should be conducted and what alternative should be researched.

Elimination by aspects

Whereas satisficing and directed cognition compare choices, elimination by aspects compares certain qualities. A person using the elimination by aspects heuristic first chooses the quality that they value most in what they are searching for and sets an aspiration level. This may be repeated to refine the search. i.e. identify the second most valued quality and set an aspiration level. Using this heuristic, options will be eliminated as they fail to meet the minimum requirements of the chosen qualities.[55]

Heuristics and cognitive effects[edit]​

Outside of searching, behavioural economists and psychologists have identified a number of other heuristics and other cognitive effects that affect people's decision making. Some of these include:

Mental accounting

Mental accounting refers to the propensity to allocate resources for specific purposes. Mental accounting is a behavioral bias that causes one to separate money into different categories known as mental accounts either based on the source or the intention of the money.[56]

Anchoring

Anchoring describes when people have a mental reference point with which they compare results to. For example, a person who anticipates that the weather on a particular day would be raining, but finds that on the day it's actually clear blue skies, would gain more utility from the pleasant weather because they anticipated that it would be bad.[57]

Herd behavior

This is a relatively simple bias that reflects the tendency of people to mimic what everyone else is doing and follow the general consensus. It represents the concept of "wisdom of the crowd".[58]

Framing effects

Stereotypes and anecdotes that act as mental filters are referred to in behavioural economics as Framing effects. People may be inclined to make different decisions depending on how choices are presented to them.[59]

Biases and fallacies[edit]​

While heuristics are tactics or mental shortcuts to aid in the decision making process, people are also affected by a number of biases and fallacies. Behavioural economics identifies a number of these biases that negatively affect decision making such as:

Present bias

Present bias reflects the human tendency to want rewards sooner. It describes people who are more likely to forego a greater payoff in the future in favour of receiving a smaller benefit sooner. An example of this is a smoker who is trying to quit. Although they know that in the future they will suffer health consequences, the immediate gain from the nicotine hit is more favourable to a person affected by present bias. Present bias is commonly split into people who are aware of their present bias (sophisticated) and those who are not (naive).[60]

Gambler's fallacy

Also known as the Monte Carlo fallacy, the gambler's fallacy is the unmerited belief that because an event occurs more frequently in the past it is less likely to occur in the future (or vice versa), despite the probability remaining constant. For example, if a coin had been flipped three times and turned up heads every single time, a person influenced by the gambler's fallacy would predict tails simply because of the abnormal number of heads flipped in the past, even though of course the probability of a heads is still 50%.[61]

Narrative fallacy

Narrative fallacy is undue influence of a presented story or "narrative." For example, a startup may get funding because investors are swayed by a narrative that sounds plausible, rather than by a more reasoned analysis of available evidence. [62]

Loss aversion

Loss aversion refers to the tendency to place greater weight on loss than disappointment. In other words, they're far more likely to try to assign a higher priority on avoiding losses than making investment gains. As a result, some investors might want a higher payout to compensate for losses. If the high payout isn't likely, they might try to avoid losses altogether even if the investment's risk is acceptable from a rational standpoint.[63]

Recency bias

When a person places greater expectation on a particular outcome simply because that outcome had just occurred, that person may be affected by recency bias. To return to the coin flipping example, given that the previous one or two flips were heads, a person affected by recency bias would continue to predict that heads would be flipped.[64]

Confirmation bias

Confirmation bias reflects the tendency to favour information or results that support one's own beliefs or values.[65]

Familiarity bias

Familiarity bias simply describes the tendency of people to return to what they know and are comfortable with. Familiarity bias discourages affected people from exploring new options and may limit their ability to find an optimal solution.[66]

Status quo bias

Status quo bias describes the tendency of people to keep things the way they are. It is a particular aversion to change in favor of remaining comfortable with what is known.

 

[recessed]

shits fucked brah

 

[Deleted member 10673]

.

 

[Chinacurry]

Pretty decent Bhai, agree with most of what u say

 

[Patient A]

data from US forex brokers' regulatory disclosures since 2010 puts the figure for failed accounts at around 75% and suggests this is typical.

80/20 rule applies to finance as well as pussy

 

[Deleted member 6403]

80/20 rule applies to finance as well as pussy

Pareto's distribution is present in every aspect of life

 

[Deleted member 12669]

Pretty decent Bhai, agree with most of what u say

He didn't say anything, he just copied and pasted this from somewhere

 

[MrGlutton]

what's your broker

 

[Deleted member 11414]

Holy fuck way too many words

 

[oldcelloser]

nice copy pasta brah

 

[Deleted member 6403]

nice copy pasta brah

It's for me to revise

 

[oldcelloser]

OP roped so obv theres no stock market for your FAYCE