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EMH

How exactly do markets adapt?█  How exactly do markets adapt? Evidence from the moving average rule in three developed markets. The Efficient Market Hypothesis (EMH) has long been an important theory in finance. Brought forth by Fama in the 1960s, the EMH suggests that it is impossible to consistently achieve returns over the average market on a risk-adjusted basis, given that price changes should only arise due to new information entering the market. According to the weak form of EMH, this information includes historical price movements. That, by extension, renders technical trading strategies based on past price data theoretically ineffective. However, the dynamic nature of financial markets has given rise to an alternative perspective known as the Adaptive Market Hypothesis (AMH), proposed by Andrew Lo in 2004. The AMH posits that the degree of market efficiency can vary over time due to the interactions of market participants, each adapting to changes within the market environment. This hypothesis allows for the potential profitability of trading rules during periods when markets are less efficient. The moving average (MA) rule serves as a litmus test for the validity of both EMH and AMH. Historically, this rule has enjoyed periods of significant predictive power, famously demonstrated by Brock, Lakonishok, and LeBaron in 1992. The primary objective of this study was to investigate the ongoing effectiveness of the moving average (MA) rule in predicting stock market prices post-1986. Andrew et al. focused on three developed markets: the DJIA in the United States, the FT30 in the United Kingdom, and the TOPIX in Japan. █  Conclusion: The study concluded that the predictive power of the MA rule has significantly diminished in all three markets examined since 1986. This decline in effectiveness aligns with the Adaptive Market Hypothesis (AMH), which posits that market efficiency is not a static condition but evolves as market participants adapt to exploiting profitable opportunities. The findings indicated that while the MA rule was once highly predictive, market participants' increased awareness and adaptation to these trading strategies likely eroded their profitability. █  Methodology ⚪ Data Set and Timeframe The study analyzed the period from 1987 to 2013, carefully selecting data from three major stock indices: the DJIA (US), the FT30 (UK), and the TOPIX (Japan). This timeframe follows the period studied in the original BLL research, allowing for a fresh evaluation of the MA rule in a contemporary market context. ⚪ Analytical Techniques Used The study used a comparative analysis of the MA rule against a traditional buy-and-hold strategy. It serves as a benchmark for market performance over time. By evaluating the returns generated by following the MA signals versus simply holding stocks, it aimed to determine the rule's effectiveness in generating excess returns. Additionally, the analysis included a detailed examination of market reactions to buy and sell signals generated by the MA rule. This approach assessed the immediate impact of these signals on stock prices and looked at how quickly and efficiently the markets absorbed this information. █ Key Findings Across all three markets studied—DJIA, FT30, and TOPIX—the findings consistently showed a decline in the predictive power of the MA rule post-1986. This trend was evident in the reduced profitability of strategies based on this rule. ⚪ Market Adaptation to Trading Signals The study revealed significant insights into how markets have adapted to trading signals. It appears that as market participants have become more sophisticated, the ability of traditional trading rules like the MA to outperform simpler strategies has decreased. This adaptation may be partly due to the increased predictability of market reactions to known trading signals, leading to quicker adjustments in stock prices. ⚪ Anticipation of MA Signals and Shift in Strategy One of the more novel findings from the study was the shift in how traders anticipate MA signals. Traders, aware of the historical profitability of these signals, have begun to preemptively act on expected signals rather than waiting for the signals to be formally generated. This anticipation leads to a scenario where actual trading on the anticipated signals the day before their formal generation often yielded superior profits compared to following the signals post-generation. This shift in strategy underscores a more proactive approach among traders, who rely on forecasting and predictive models to stay ahead of traditional signal-generation techniques. █ Implications for Market Participants The findings suggest that traders who have relied heavily on MA strategies should reassess their trading approaches. While MA strategies may not need to be completely discarded, they should be used with a grain of salt alongside other comprehensive tools for analysis. The decreased predictability of returns using MA rules supports the Efficient Market Hypothesis (EMH). This confirms the hypothesis that markets may efficiently reflect all known information, including known trading strategies like MA, thus negating their effectiveness over time. On the other hand, the study strongly supports the Adaptive Market Hypothesis (AMH), emphasizing that market efficiency is not a static state but varies over time with the actions of market participants. The AMH's view that trading strategies can ebb and flow in effectiveness depending on market conditions is corroborated by the varying success rates of MA strategies over different periods and markets. In the context of moving averages, which are often used to identify trends by smoothing out price data over a specified period, their effectiveness can change. For instance, in a highly volatile market, MA strategies might generate many false signals, leading to poor performance. Conversely, in a trending market with less volatility, MA strategies could be quite successful. This variation in success rates across different times and market environments supports the AMH view that the profitability of trading strategies can fluctuate as market dynamics evolve. Trend Consolidation █ Study Limitations While the study provides insightful findings, it has certain limitations that should be noted. Firstly, focusing on only three developed markets—DJIA, FT30, and TOPIX—may not fully represent global market dynamics. The behaviors and trends in these markets might not be universally applicable, especially in less developed or emerging markets. Additionally, the study's methodology does not account for transaction costs, which could significantly impact the profitability and practical application of MA strategies in a real-world trading environment. █  Reference Urquhart, A., Gebka, B., & Hudson, R. (2015). How exactly do markets adapt? Evidence from the moving average rule in three developed markets. Journal of International Financial Markets, Institutions & Money, 38, 127-147. doi:10.1016/j.intfin.2015.05.019 ----------------- Disclaimer This is an educational study for entertainment purposes only. The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
COINBASE:BTCUSDEducation
by Zeiierman
3
Market Microstructure: An Extensive AnalysisI. Introduction Market microstructure, a specialized area within finance, explores the intricate mechanisms involved in trading within financial markets. It focuses on how trades occur, the interplay between prices and information, and how these interactions collectively shape market dynamics. Understanding market microstructure enables investors, traders, financial institutions, and regulatory bodies to comprehend the process of price formation, make informed trading decisions, design effective trading strategies, and develop sound financial regulations. II. Theoretical Foundations Three fundamental theories underpin market microstructure: The Efficient Market Hypothesis (EMH), the Random Walk Hypothesis, and the theory of Information Asymmetry. Each theory provides a unique perspective on the functioning of financial markets. Efficient Market Hypothesis (EMH): The EMH, introduced by Eugene Fama, posits that financial markets are "informationally efficient," with asset prices instantaneously reflecting all available information. According to the EMH, consistently outperforming the market is impossible without assuming additional risk, since every piece of information that could potentially affect the price of an asset is already factored into the current price. There are three forms of market efficiency according to the EMH: weak, semi-strong, and strong, each reflecting the extent of the efficiency. Random Walk Hypothesis: The Random Walk Hypothesis suggests that price changes in securities are independent and identically distributed, meaning that past movements or trends cannot predict future price movements. In essence, securities prices follow a 'random walk', making it futile to predict future prices based on historical data. Information Asymmetry: This theory points to the situation where one party has more or better information than another. In financial markets, information asymmetry creates a dynamic where informed traders (insiders) can potentially exploit their information advantage over uninformed traders, disrupting market efficiency. III. Role of Market Makers Market makers play a pivotal role in financial markets, facilitating transactions by constantly quoting bid (buy) and ask (sell) prices for financial instruments. Their constant presence in the markets helps maintain liquidity and market efficiency. Market makers are compensated for their services through the bid-ask spread - the difference between the bid price and the ask price. This spread represents the market maker's profit and compensates them for the risk they undertake in holding a particular security in their inventory, which might decrease in value. IV. Order Flow and Price Discovery Order flow, the process by which buy and sell orders are executed in the market, is integral to price discovery - the mechanism that determines the price of an asset in the marketplace. Analyzing order flow can provide valuable insights into trading activity and market sentiment. When a large order hits the market, it can significantly impact a security's price, creating price volatility. Understanding order flow is therefore essential for managing risk, providing liquidity, and effectively navigating the market. V. High-Frequency Trading (HFT) High-frequency trading (HFT) employs advanced algorithms to execute large volumes of trades in microseconds. HFT can improve market efficiency and liquidity by reducing bid-ask spreads, rapidly processing new information, and providing additional liquidity to the market. However, HFT also has potential drawbacks. Its speed can raise issues around fairness, with HFT firms potentially exploiting their speed advantage to the detriment of slower market participants. It may also increase market volatility and contribute to market instability, as evidenced by instances of 'flash crashes.' VI. The Impact of Information Flow Information plays a pivotal role in financial markets. Two categories of information that impact trading and investment decisions are public and private information. Public Information: This includes macroeconomic data, corporate earnings reports, policy changes, and other marketnews that are equally accessible to all market participants. When this information is released, markets adjust as participants process and respond to the new information, causing immediate and often significant price changes. Understanding the dynamics of how public information impacts price can provide traders with an edge in predicting and navigating market reactions. Private Information: This refers to non-public or unequally distributed information among market participants. Informed traders, who might have access to private information, can use it to their advantage, resulting in potential profits. However, this leads to information asymmetry, which can disrupt market efficiency and fairness as it creates an imbalance of knowledge among market participants. The impact of information flow on market prices is significant. Rapid adjustments to new information keep the markets efficient, but they also introduce volatility. Information asymmetry can lead to market distortions and manipulative practices like insider trading. Therefore, understanding the flow of information is key to comprehending market microstructure. VII. Market Microstructure Models Several market microstructure models have been developed to better understand the relationship between information asymmetry, price determination, and market participant interaction: The Sequential Trade Model: This model, also known as the "dealer model," posits a single dealer who trades with many customers. Dealers, who are assumed to be less informed than their customers, adjust their prices based on the order flow. For instance, an unexpected surge in buy orders would lead the dealer to infer that customers might have positive private information, and therefore, they increase the price to offset potential adverse selection risk. The Strategic Trade Model: This model focuses on traders who tactically time their trades to maximize their expected profit. They consider the potential impact of their trades on future prices and act accordingly. For instance, a trader with private information about a forthcoming price rise might initially trade smaller quantities to prevent any significant price impact that could reveal their information. The Market Making Model: In this model, multiple market makers compete for customer orders, and prices are determined based on this competitive dynamic. The market-making model allows for a more realistic market scenario where competition, rather than a single monopoly dealer, drives price adjustments. These models offer valuable insights into the complex process of trading and price formation in financial markets. VIII. Regulatory Implications Understanding market microstructure is crucial for financial market regulators. They must ensure that markets remain fair and efficient while also being conducive to innovation and competitive market making. With the growing complexity and speed of financial markets—especially with the rise of algorithmic and high-frequency trading—regulators face the challenge of managing the delicate balance between allowing market innovation and preventing practices that might lead to market instability or unfair advantages. IX. Future Directions As technology continues to transform financial markets, market microstructure's importance in comprehending these changes cannot be overstated. The rise of digital assets like cryptocurrencies, the growing use of machine learning and artificial intelligence in trading, and the proliferation of decentralized finance (DeFi) platforms all necessitate a deep understanding of market microstructure. New theoretical and empirical models will likely emerge to explain phenomena that are not well understood today, further deepening our understanding of market dynamics. Similarly, the regulatory landscape will continue to evolve in response to these changes, making the study of market microstructure crucial for informed policy-making. X. Conclusion Market microstructure is a crucial field in finance that examines the intricacies of trading in financial markets. Understanding how market makers function, the strategies of high-frequency traders, the impacts of information asymmetry, and how asset prices are formed is essential for participants across the financial landscape. As technological advancements continue to transform the financial industry, insights offered by market microstructure will be of vital importance in navigating these changes. The field will continue to grow in relevance, contributing to more efficient, fair, and resilient financial markets. I hope that you find this information valuable, if you have any questions feel free to drop them in the comments. Enjoy!
BITSTAMP:BTCUSDEducation
by BiggWigg
Unraveling Efficient Market HypothesisMany believe that a well-defined, simple, and robust trading strategy can help a trader acquire gains that outperform the market or purchase undervalued stocks in hopes of outsized returns upon rebound, but is this the case? Students of the Efficient Market Hypothesis (EMH) would argue that fundamental and technical analysis are pointless approaches to the market that are merely a mirage of a self-fulfilling prophecy. EMH is a cornerstone of modern financial theory, which posits that markets are perfectly efficient and always reflect all available information. The influence of EMH is pervasive, guiding investment strategy and shaping financial regulation. There is growing skepticism among academics and traders about the accuracy and efficacy of EMH in modern markets. EMH is a dense topic, but we will do our best to dive into what EMH is, its strengths, and its limitations in modern times. Understanding EMH To understand what EMH is, we need to understand the forms of EMH, of which there are three levels of efficiency: weak, semi-strong, and strong. The weak form of EMH suggests that current prices reflect all past trading information, including past prices. Thus rendering fundamental analysis and technical analysis moot and impossible to beat the market. Semi-strong EMH argues that the current price accounts for all public data and does not include private data. Again, fundamental and technical analysis will not be fruitful in helping traders outpace market returns. The strong form of EMH posits that prices reflect all available information, including insider information. In Support Of and Against EMH Supporters of EMH argue that markets are efficient because of the excess number of rational investors, and the competition among them (bulls vs. bears) ensures that prices are always accurate. The more market participants there are, the more efficient a market becomes as it becomes increasingly competitive and more price information becomes available. The competitive nature and increased liquidity of the market shows that it is difficult, at best, to consistently outperform the markets. Opponents of EMH argue that human biases and irrational behavior can lead to market inefficiencies. Investors often make irrational decisions based on emotions and cognitive biases. This is tough to argue, given the countless articles and books on market psychology. Market anomalies, such as the value and momentum effects, also suggest that markets are not perfectly efficient. Historical market events, such as the 2008 financial crisis or other perceived “bubbles,” further question the assumptions of EMH. Practical Implications and Real-World Observations Despite EMH, some investors have consistently outperformed the market; famously among them is Warren Buffet. Some hedge funds have also been successful in beating market benchmarks. One could argue that though a market is efficient, there are individuals who are statistical anomalies that have outperformed the market under EMH theory. Market inefficiencies and opportunities exist in specific asset classes or regions, such as emerging markets or distressed debt-stricken economies, but an easily observable form of market inefficiency is arbitrage trading. Wherein traders buy and sell to exploit minute price discrepancies of assets between exchanges. Alternative Approaches It is hard to objectively believe that one can not formulate a system that helps a trader make returns that outpace the market. Fundamental analysis and technical analysis are two approaches to investing that challenge the assumptions of EMH. Fundamental analysis involves examining company-specific information and valuations to find undervalued stocks which is entirely conflicting with EMH theory. While technical analysis involves using price patterns and indicators for market timing in hopes of profits in your chosen trade direction. The Future of Market Efficiency The rise of technology, such as high-frequency trading, trading algorithms, and artificial intelligence, is changing the landscape of financial markets. Some argue that technology is making markets more efficient; others would suggest that it is introducing new sources of market inefficiencies. Will the definitive parameters of what EMH need to be adjusted as the markets evolve? Only time and people with significantly larger brains than I will tell. Conclusion EMH remains a principal concept in modern finance, but not without limitations and challenges. It is paramount for traders to understand what EMH is, even if they rely on different analysis theories to make their own trading decisions. Investors should adopt a flexible and adaptive approach to investing, recognizing that markets are not always perfectly efficient and that opportunities for outperformance exist. Ultimately, we believe the key to successful investing is a combination of sound strategy, disciplined execution, and a willingness to learn and adapt. If you like this post, don't forget to drop a boost and follow our page for more educational content!
DJ:DJIEducation
by LeafAlgo
3
VFF Healthy consolidation. No matter how fundamentally strong VFF Is and how solid their execution is , the overall volatility and psychology of the cannabis markets will effect share price. Personally I believe VFF to be very well positioned, in a strong position financially and their operational plans are on track for 2018 and 2019. Short term VFF is in for a tough battle up, overall bulls seem to be exhausted and the bears have been exerting some serious selling pressure both on VFF as an individual stock and the Canadian cannabis market as a whole. From a technical analysis standpoint VFF has immense potential to retrace back to previous highs and maintain them, this would require the bulls to pick up steam. I'm looking for industry wide catalysts and also VFF specific catalysts to move this stock. My time frame for the retracement back to $8.50+ is 2 months
VLong
by navpowarr
EMERALD HEALTH THERAPEUTICS INC TSXV:EMH - CORRECTION OVER?EMERALD HEALTH THERAPEUTICS INC TSXV:EMH - CORRECTION OVER? According to my current count of this cycle we are finishing wave 5, third wave was the longest and 1 and 5 are very close. I'm currently looking at a ABC correction finishing, although it only reached .5 fib level according to this count. Could correct further to the $4 level. Holding out on buying more for now. Disclaimer - Novice trader learning technical analysis, any advise is welcomed. Do not take this as financial advice. Thanks! Good luck.
E
by Advin