Prediction of Stock Market Index Using Genetic Algorithm

The generation of profitable trading rules for stock market investments is a difficult task but admired problem. First stage is classifying the prone direction of the price for BSE index (India cements stock price index (ICSPI)) futures with several technical indicators using artificial intelligence techniques. And second stage is mining the trading rules to determined conflict among the outputs of the first stage using the evolve learning. We have found trading rule which would have yield the highest return over a certain time period using historical data. These groundwork results suggest that genetic algorithms are promising model yields highest profit than other comparable models and buy-and-sell strategy. Experimental results of buying and selling of trading rules were outstanding. Key words: Data mining, Trading rule, Genetic algorithm, ANN, ICSPI predictio

Tactical allocation in commodity futures markets: Combining momentum and term structure signals

This paper examines the combined role of momentum and term structure signals for the design of profitable trading strategies in commodity futures markets. With significant annualized alphas of 10.14% and 12.66%, respectively, the momentum and term structure strategies appear profitable when implemented individually. With an abnormal return of 21.02%, our double-sort strategy that exploits both momentum and term structure signals clearly outperforms the single-sort strategies. This double-sort strategy can additionally be utilized as a portfolio diversification tool. The abnormal performance of the combined portfolios cannot be explained by a lack of liquidity, data mining or transaction costs

Technical Analysis-Based Data Mining Strategies for Stock Market Trend Observation

This study introduces a comprehensive approach that utilizes technical analysis-based data mining strategies to observe and predict stock market trends, by leveraging historical trading data, technical indicators such as moving averages, RSI, and MACD, to systematically analyze and interpret market behavior, thereby providing investors and traders with actionable insights for making informed decisions in the volatile environment of stock trading. By integrating quantitative analysis with predictive modeling, the methodology aims to enhance the accuracy of trend forecasts and identify profitable trading opportunities. Through the application of cross-validation and backtesting techniques, the effectiveness of these strategies is rigorously evaluated against actual market movements, offering a robust framework for risk management and portfolio optimization. This interdisciplinary approach not only demystifies the complexities of the stock market but also opens new avenues for research and development in financial technology, promising a significant contribution to the field of economic forecasting and investment strategy

Can profitable trading strategies be derived from investment best-sellers?

A glance along the finance shelves at any bookshop reveals a large number of books that seek to show readers how to ‘make a million’ or ‘beat the market’ with allegedly highly profitable equity trading strategies. This paper investigates whether useful trading strategies can be derived from popular books of investment strategy, with What Works on Wall Street by James P. O'Shaughnessy used as an example. Specifically, we test whether this strategy would have produced a similarly spectacular performance in the UK context as was demonstrated by the author for the US market. As part of our investigation, we highlight a general methodology for determining whether the observed superior performance of a trading rule could be attributed in part or in entirety to data mining. Overall, we find that the O'Shaughnessy rule performs reasonably well in the UK equity market, yielding higher returns than the FTSE All-Share Index, but lower returns than an equally weighted benchmar

Technical analysis in the foreign exchange market

This article introduces the subject of technical analysis in the foreign exchange market, with emphasis on its importance for questions of market efficiency. Technicians view their craft, the study of price patterns, as exploiting traders’ psychological regularities. The literature on technical analysis has established that simple technical trading rules on dollar exchange rates provided 15 years of positive, risk-adjusted returns during the 1970s and 80s before those returns were extinguished. More recently, more complex and less studied rules have produced more modest returns for a similar length of time. Conventional explanations that rely on risk adjustment and/or central bank intervention are not plausible justifications for the observed excess returns from following simple technical trading rules. Psychological biases, however, could contribute to the profitability of these rules. We view the observed pattern of excess returns to technical trading rules as being consistent with an adaptive markets view of the world.Foreign exchange rates

The Halloween Indicator is More a Treat than a Trick

This paper uses stock market returns (2007-2015) and confirms the existence of Halloween effect anomaly after the 2008 financial crisis. Findings suggest that the Halloween effect can still be observed in 34 out of the 35 countries. A more aggressive trading strategy of shorting the market during summer and taking a long position in winter yields 4.77% more than the buy-and-hold strategy. A new explanation is offered for the persistence of the Halloween effect. A positive feedback between investors’ belief and behavior causes the market to underperform in the summer and recover in the winter, resulting in a self-fulfilling prophecy

Automated Trading Systems VS Manual Trading in Forex Exchange Market

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the recent decades, automated trading has been widely used in Forex and Money Markets, as well as in financial markets. This auto trading provided substantial benefits to transaction efficiency. Many trading robots have been created to substitute humans, capable of simulating trading strategies and continuously making profits. Nevertheless, programs cannot reproduce all human behaviour and most robots are over-sensitive, therefore, it is difficult to have the same results as human traders. The study focuses on evaluating the trading machines sensitivity and effectiveness. The economic markets can benefit from the machine in several ways, through continuous operation, increasing diversification, short/term trading opportunities and by forecasting opportunities e. g. currency price changes. The further investigation indicates that the majority of forex trading robots are profitable, in fact, there is a great tendency for curve-fitting or data-mining. There are some impressive robots out there; of course, these systems maintain an advantage and successfully manage risk. The best ones are more about position sizing and cutting losses quickly and less about high win rates. The greater the sensitivity the greater the trading opportunities, but this decreases the performance. This research will contain interviews with experts that will validate the study