High-frequency trading
Pairs trade
Alternative trading system
Electronic trading
Stock exchange
Best insight from top research papers
The most profitable proven trading strategy appears to be momentum investing, which has consistently earned non-zero returns over time . This strategy involves selecting stocks based on their past performance over a specific time period, such as two to twelve months. It has been observed to earn average monthly returns of 1.57% over the period from 1965 to 2014, and even larger returns of 2.27% in the most recent ten-year period . However, it is important to note that the profitability of this strategy may be affected by transaction costs, especially when trading in smaller stocks . Nevertheless, a strategy that focuses only on the winner portfolio and avoids trading the loser portfolio has been found to earn strong and persistently abnormal profits, even after accounting for transaction costs .
Related Questions
What is the most profitable trading strategy?4 answersAmong the various strategies studied, momentum investing appears to be the most profitable trading strategy. It consistently earns non-zero returns over a long time period, with an average monthly return of 1.57% from 1965 to 2014. In recent years, the return has been even larger at 2.27% per month. However, profitability net of transaction costs is weak due to trading in small stocks with high transaction costs, especially for the loser portfolio. To overcome this, a strategy that focuses only on the winner portfolio and avoids selling the costly loser portfolio can still earn strong and persistently abnormal profits, even after transaction costs.What are the most profitable investment strategies?5 answersVarious investment strategies were modeled and examined for their tradeoffs between risk and return. The strategies included combinations of stocks and bonds, with some strategies switching between investment types, others transitioning, and some changing based on stock market performance. Another profitable investment strategy involves using a speculative download mechanism in BitTorrent private communities, which identifies swarms with high upload potential and automatically downloads and seeds them. This strategy resulted in a median 207% return on investment. Additionally, portfolio management strategies based on spillover indices can provide useful information for maximizing portfolio value in Central and Eastern European and Southern and Eastern European stock markets. For with-profit life insurance contracts, different investment strategies were evaluated, resulting in a discount in the certainty equivalent rate of return for giving up intergenerational risk transfer. Finally, investing in sovereign bond funds representing high GDP-per-capita countries has been found to be a profitable strategy, with no strong relationship between sustainability and GDP per capita.What are the most stable and effective trading strategies?5 answersThere are several trading strategies mentioned in the abstracts that can be considered stable and effective. One paper discusses the use of technical analysis and trading rules based on historical market data to identify trends and make investment decisions. Another paper explores the effectiveness of trading strategies in the Taiwan stock market and finds that none of the 21 strategies tested were effective, supporting the efficient market hypothesis. Additionally, a paper on radio resource management proposes a novel approach of radio spectrum trading via derivatives contracts to address short-term demands for spectrum while ensuring quality of service requirements are met. These papers provide insights into different trading strategies and their effectiveness in various contexts.What are some of the strategies that can be used to trade forex?3 answersThere are several strategies that can be used to trade forex. One approach is to analyze fundamental factors such as economic indicators and news events to determine the direction of currency movements. Another strategy involves using technical analysis, which involves studying historical price patterns and using indicators to identify potential entry and exit points. Additionally, machine learning algorithms such as Random Forest and SVM can be employed to model forex price movements and make trading decisions. These algorithms can utilize classic technical indicators like moving averages as features to improve trading performance. Furthermore, risk management strategies such as forward currency contracts, currency options, and cross-currency hedges can be employed to manage foreign exchange risk. Overall, a combination of fundamental analysis, technical analysis, machine learning algorithms, and risk management strategies can be used to trade forex effectively.What is trading strategy?3 answersA trading strategy refers to a systematic plan or approach used by individuals or firms to make decisions about buying and selling financial assets. It involves analyzing market conditions, identifying trends, and generating signals to determine when to enter or exit trades. Trading strategies can be based on various factors, such as machine learning algorithms, technical indicators, or market state classification. They aim to yield higher returns while managing risks effectively. Different trading strategies have been proposed and tested, including trend-following strategies, Dogs of the Dow Theory, and mean-variance portfolio construction with risk constraints. These strategies have been studied in different contexts, such as the circular economy, algorithmic trading, and stock price prediction..What is a trading strategy in forex?4 answersA trading strategy in forex refers to a set of rules or guidelines that traders use to make decisions about buying or selling currency pairs in the foreign exchange market. These strategies can be developed using various methods and techniques. For example, one approach is to use a genetic algorithm to evolve profitable trading rules based on weighted moving averages. Another approach involves processing and validating trading strategies received from users, transforming them into logical code blocks, and executing trade orders based on external data. Additionally, quantile regression can be used to estimate the conditional distribution functions of stock market returns, which can then be used to generate buying and selling signals. Trading strategies can also be analyzed in terms of correlations between institutions' trading patterns, which can reveal community structures and preferential lending relationships.
