The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Genetic Algorithms and Investment Strategy Development

The aim of this paper is to investigate the use of genetic algorithms in investment strategy development. This work follows and supports Franklin Allen and Risto Karljalainen’s previous work1 in the field, as well adding new insight into further applications of the methodology. The paper first examines the capabilities of the algorithm designed in Allen and Karjalainen’s work by using human‐developed (rather than market‐historical) datasets to determine whether the algorithm can detect simple signals; the results show that the algorithm is quite capable of such basic tasks. Next, the S&P 500 test performed in Allen and Karjalainen’s original work was confirmed. Then, experiments were conducted in emerging equity markets, as well as commodities markets with a range of fundamental as well as technical indicators. The results generally show no significant positive excess returns above a buy‐and‐hold strategy; speculations for possible reasons are discussed. In addition, suggestions for future research endeavors are presente

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances

The Determinants of Bankruptcy for Chinese Firms

The global financial crisis in 2008 increased the number of business failures in the U.S. as well as in China. The Chinese economy has also been affected by the recent global financial crisis given the fact that the Chinese economy depends heavily on international trade. Our study tries to find the determinants of bankruptcy in Chinese firms. Both logit and survival model analyses provide consistent results on the determinants in predicting distressed firms in China. Our results suggest that firms with liquidity problems and firms experiencing a decline in profits are more likely to file for bankruptcy. In addition, we find that, compared to state-owned enterprises (SOEs), collectively-owned enterprises, private-owned enterprises, and foreign-owned businesses are more likely to file for bankruptcy. This conclusion is robust after controlling for regional differences. The findings of this study show that the financial variables developed by Altman [Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(3), 589–609] and Ohlson [Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131] perform reasonably well in determining business failures of Chinese firms even though SOEs and shadow financing exist in China

Forecasting stock market return with nonlinearity: a genetic programming approach

The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA

Success Stories in Asian Aquaculture

The stories presented in this book reflect the unique nature of Asian aquaculture, providing first-time insight into how and why it has become so successful. Overall, the book demonstrates how the resiliency, adaptability, and innovation of small-scale aquaculture farmers have been crucial to this success. It also places aquaculture development in Asia into a wider global context, and describes its relationship to natural systems, social conditions, and economics. The book is unique in its in-depth presentation of primary research on Asian aquaculture, and in demonstrating how aquaculture can have a lasting positive impact on livelihoods, food security, and sustainable development

Forecasting EPS of Chinese Listed Companies Using Neural Network with Genetic Algorithm

In this paper we use neural network models to forecast earnings per share (EPS) of Chinese listed companies using fundamental accounting variables. The sample includes 723 Chinese companies in 22 industries over 10 years. The result shows that the neural network model with weights estimated with genetic algorithm (GA) outperforms the neural network with weights estimated with back propagation (BP). Results also show that the addition of fundamental accounting variables used in the neural network models further improves the forecasting accuracy