Companies

Net earnings after pre-tax profits. Profiles include: Anadarko Petroleum, Chevron Texaco, Conoco Phillips, Exxon Mobil, Occidental, Schlumberger, YPF SA, EnCana Corporation, Imperial Oil, Petroleo Brasileiro, Total SA, E.ON, ENI, Repsol, Ecopetrol SA, Norsk Hydro, Statoil, Devon Energy Corporation. BP, Shell, BHP Billiton, CNOOC, Sinopec and PetroChina. Updated on a monthly basis.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

铁路冷藏运输运价分析

本文对各种运输方式易腐货物的运价进行了比较分析,对铁路冷藏运输的现行运价和保本运价进行了讨论,提出了铁路冷藏运输运价改革的若干建议,可供铁路进行冷藏运输改革参考。     

Benchmarking of electricity networks: Practical problems with its use for regulation

Benchmarking is a technique derived for management purposes, intended to identify outliers in any population as possible problems for resolution. Several regulatory agencies have tried to use benchmarking to define the degree of inefficiency of regulated companies, by reference to some target or frontier. This paper identifies the main problem inherent in trying to adapt a management technique to a regulatory purpose – namely that it requires the exercise of subjective judgements. The resulting lack of predictability and objectivity is not conducive to the provision of efficient regulatory incentives.This paper explains where subjective judgement enters into regulatory applications of benchmarking, which is not always apparent. The choice of model and selection of data sets are two obvious areas, but the main problem arises over the assumption that any costs not explained by the model must be due to inefficiency. Such assumptions are simply unsupported by evidence and lead to cost targets (or cost reduction targets defined over several years) that are little better than subjective guesses. There are other, superior ways to set cost reduction targets, based on long-term trends in total factor productivity for the regulated sector, which are used in the US and in other countries. However, some regulators feel obliged to use benchmarking and the paper concludes with recommendations as to how benchmarking should support further and more objective investigations into the costs of regulated utilities.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

Pricing strategies with reference effects in competitive industries

This paper examines the effect of reference prices on companies operating within competitive industries. We confirm that even with competition, firms optimally price high in the short term to generate a high reference price and then decrease this price over time. Competitors' prices converge toward each other over time, emphasizing the short‐term nature of reference prices. We then show that pricing optimally to take advantage of reference prices generates a positive externality for other firms in an industry, such that competitors may generate higher profit. The longer the focus of a given firm, the more profit the firm generates, but less relative to its competitors. This arises because the externalities created through pricing high to increase reference prices outweigh the benefits of the higher reference prices themselves. If pricing managers are compensated relative to their competition, this suggests that short‐termism may be implicitly encouraged to the detriment of profit.     

利率政策对西安房价影响的有效性分析

由于经济发展水平不同,居民购买力水平不同,货币政策调控对不同地区房价的有效性也存在差异,需要根据不同地区的特点进行分析.以西安市房价变化为例,选取2003~2007年的数据,分析利率政策调控对西安房价影响的有效性并进行了实证检验,发现利率政策实施对西安市房地产价格的影响非常有限,2003~2007年的利率政策调控并没有达到预期效果,只是使市场发生了轻微变动.为此,提出了提高利率、提升地方政府公共服务能力等相关的政策建议.