Using Fundamental Analysis And An Ensemble Of Classifier Models Along With A Risk Off Filter To Select Outperforming Companies
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Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies
This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.
Asset Allocation and Machine Learning
The purpose of this paper is to benchmark a large set of eight contemporary machine learning algorithms in order to identify the best model for the selection of outperforming stocks in Switzerland. The selection process is modelled as a one-year ahead direction-prediction of stocks' excess returns. 65 fundamental, macroeconomic and technical variables are applied to perform the predictions and characterise the analysed 255 publicly listed Swiss companies between 2001 and 2019. The algorithms are compared with regards to their predictive power in the Swiss Performance Index (SPI). Additionally, the models' feature selection is derived and the most significant variables are analysed and discussed. Ultimately, a backtest is performed to verify the profitability of the predictions. The results indicate that ensemble models, namely XGBoost and Random Forest, are the best performing algorithms, selecting outperforming stocks with an accuracy above 80%. Furthermore, the feature selection analysis shows that the most important variables are similar throughout the best performing algorithms, creating a defining effect on the performance. Lastly, when backtested, the best two models yield average excess returns above 30%. This study contributes to extant literature, as it is the first to make such an extensive benchmark in general and specifically on a country level basis in Switzerland.
Mastering Fundamental Analysis
Fundamental analysis is crucial to developing and maintaining a rewarding share portfolio. By studying and understanding the economic climate, industry conditions and the financial health of specific companies, investors will develop the analytical skills necessary for making profitable investment decisions.First published in the USA, Michael C. Thomsett? Mastering Fundamental Analysis: How to Spot Trends and Pick Winning Stocks Like the Pros is reproduced here in full. Many of the examples describe the intricacies of the American marketplace. Yet the book? relevance to the Australian sharemarket is still invaluable. William S. Harper, a respected author on Australian financial topics, has written the foreword. Here, he suggests Mastering Fundamental Analysis is for every serious investor who wants more control and reasoning behind their investment choices.In clear and concise terms this book teaches the reader how to:* study financial statements* interpret and understand market trends* use market ratios and indices profitably* make informed decisions based on real information not idle rumour.This is not a get-rich-quick book; there is no magic formula. Instead, by explaining in simple, straightforward language the rigours of fundamental analysis, it shows investors how to get informed quickly. The theory is that good information leads to good decision making. And good decision making ultimately leads to good profits.