A novel approach to exploring company’s financial soundness: Investor’s perspective

Prediction of company’s life cycle stage change; creation of an ordered 2D map allowing to explore company’s financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique “t-distributed stochastic neighbor embedding”.Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company’s financial soundness from a rating agency perspective. The proposed use of information not only from companies’ financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody’s rating agency.Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company’s management team. It was also found that company’s life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging.     

Exploring the dynamic model of the returns from value stocks and growth stocks using time series mining

This study considered that value stocks and growth stocks are 2-dimensional concepts. We defined the book-to-market ratio as the value factor and the return on equity as the growth factor. We used these 2 factors to divide stocks into 4 types: high-value, low-value, high-growth, and low-growth stocks. Furthermore, we explored the change in stock prices and stock returns for these 4 categories before and after the formation of investment portfolios. We also established a dynamic model showing the returns from value stocks and growth stocks, called the exponential decay model. Finally, we used Taiwan Stock Exchange data to examine effectiveness of the model during the period from 1995 to 2009. The results are as follows: first, high-value stocks and low-value stocks exhibit a significantly over-reacting phenomenon. Second, high-growth stocks and low-growth stocks exhibit an obviously under-reacting phenomenon. Third, in each current quarter, high-value stocks exhibit the lowest returns; however, in the subsequent quarter, they have the highest returns, and then demonstrate a slow declining trend in the following quarters. These results showed that the stock market can exhibit a dramatic response to extraordinary information and proved that the stock market requires considerable time to correct themselves from an excessive reaction, thus high-value stocks exhibited a higher return. Fourth, in each current quarter, high-growth stocks had the highest return, followed by a rapidly decreasing trend in the following quarters. The t + 3 quarter returns were lower than those of low-growth stocks. This result demonstrated that the stock market does not exhibit an adequate reaction, but still remains rather efficient for routine financial information. Finally, regardless of value stocks or growth stocks, exponential decay models could accurately match with the data.     

Trading With a Stock Chart Heuristic

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently ldquobeat the marketrdquo because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern - the ldquobull flag stock chartrdquo - which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York stock exchange composite index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the ldquobull flagrdquo (or ldquobear flagrdquo stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice.     

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.     

The economics of stock touting during Internet‐based pump and dump campaigns

Information systems have facilitated the increase in relevance of financial markets. Nevertheless, the rise of the Internet has eased information‐based financial market manipulations. In this study, we examine the phenomenon of stock touting during pump and dump campaigns, in which deceivers advertise stocks to profit from an increased price level. We observe that the positive prospects promised are not confirmed by corporate disclosures and financial news. Furthermore, manipulators select targeted financial instruments based on specific stock and company characteristics. Manipulators avoid signals of anomaly and prefer unknown stocks. We find that stock touting has a positive market impact but that it is followed by a large decline in stock price in the subsequent days, causing investors to lose substantial amounts of their investments. We consider the impact of information generation, information content, and information presentation on the corresponding market reaction. Interestingly, information generation influences the demand for the stock, but information content and information presentation drive the willingness to pay. Our results are highly relevant for Internet users, software vendors, and market surveillance authorities, as a deep understanding of such information‐based manipulations is necessary to develop appropriate countermeasures.     

Mining stock price using fuzzy rough set system

In this study of mining stock price data, we attempt to predict the stronger rules of stock prices. To address this problem, we proposed an effective method, a fuzzy rough set system to predict a stock price at any given time. Our system has two agents: one is a visual display agent that helps stock dealers monitor the current price of a stock and the other is a mining agent that helps stock dealers make decisions about when to buy or sell stocks. To demonstrate that our system is effective, we used it to predict the stronger rules of stock price and achieved at least 93% accuracy after 180 trials.     

Determining of stock investments with grey relational analysis

Selecting stock is important problem for investors. Investors can use related financial ratios in stock selection. These kind of worthy financial ratios can be obtained from financial statements. The investors can use these ratios as criteria while they are selecting the stocks. Since dealing with more than one financial ratio, the investing issue becomes multi-criteria decision making (MCDM) problem for the investors. There are various techniques for solving MCDM problems in literature. In this study grey relational analysis (GRA) is used for ordering some financial firms’ stocks which are in Financial Sector Index of Istanbul Stock Exchange (ISE). Besides, because of the importance of criteria weights in decision making, three different approaches – heuristic, Analytic Hierarchy Process, learning via sample – were experimented to find best values of criteria weights in GRA process.     

A generalized model for financial time series representation and prediction

Traditional financial analysis systems utilize low-level price data as their analytical basis. For example, a decision-making system for stock predictions regards raw price data as the training set for classifications or rule inductions. However, the financial market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Raw price data are too random to characterize determinants in the market, preventing us from reliable predictions. On the other hand, high-level representation models which represent data on the basis of human knowledge of the problem domain can reduce the randomness in the raw data. In this paper, we present a high-level representation model easy to translate from low-level data into the machine representation. It is a generalized model in that it can accommodate multiple financial analytical techniques and intelligent trading systems. To demonstrate this, we further combine the representation with a probabilistic model for automatic stock trades and provide promising results. An erratum to this article can be found at     

Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems

Modern computerized stock trading systems (mechanical trading systems) are based on the simulation of the decision-making process and generate advice for traders to buy or sell stocks or other financial tools by taking into account the price history, technical analysis indicators, accepted rules of trading and so on. Two stock trading simulating systems based on trading rules defined using fuzzy logic are developed and compared. The first is based on the so-called “Logic-Motivated Fuzzy Logic Operators” (LMFL) approach and aims to avoid certain disadvantages of the classical Mamdani’s method, which has been developed for use in fuzzy logic controllers and not for solving the decision-making problems of stock trading. The LMFL   approach is based on the modified mathematical representation of t$t$-norm and Yager’s implication rule. The second trading system combines the tools of fuzzy logic and Dempster–Shafer Theory (DST  ) to represent the features of the decision-making process more transparently. The fuzzy representation of trading rules based on the theory of technical analysis is used in these expert systems. Since the theory of technical analysis is based on the indicators used by experts to predict stock price movements, the method maps these indicators into new inputs that can be used in a fuzzy logic system. The only required inputs to calculate these indicators are past sequences (history) of stock prices. The method relies on fuzzy logic to choose an appropriate decision when certain price movements or certain price formations occur. The optimization procedure based on historical (teaching) data is used as it significantly improves the performance of such expert systems. The efficiency of the developed expert systems is measured by comparing their outputs versus stock price movements. The results obtained using real NYSE$NYSE$ data allow us to say that the developed expert system based on the synthesis of fuzzy logic and DST provides better results and is more reliable. Moreover, such a conjunction of fuzzy logic, DST and technical analysis, makes it possible to make a profit even when trading against a dominating trend.     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

The influence of investor's behavioral biases on the usefulness of the Dual Moving Average Crossovers

The so called Dual Moving Average Crossovers are said to be useful signals for forecasting trends of stock prices, as one of the technical analysis methods. First, we examined the usefulness of these crossovers by using historical daily closing price data and tick by tick price data of Japanese stocks. The results revealed that these crossovers were useful as confirmatory signals for forecasting market trends. Second, we tried to identify the underlying reasons for the usefulness of the crossovers. A model, which followed the Efficient Market Hypothesis, was found to fail to generate the price fluctuation where the crossovers were useful. We then developed a model that incorporated investor's suspicion about current price validity and two famous behavioral biases: conservativeness and representativeness. We identified the mechanism that those crossovers were closely related to investor's suspicion and the behavioral biases. Kotaro Miwa: He is a Ph.D. candidate at the University of Tokyo. He is also a quantitative financial analyst and fund manager at Tokio Marine Asset Managements. He received his B.A. degree from the Faculty of Engineering at the University of Tokyo in 2001. He also received M.A. degree from the Department of Systems Science at the University of Tokyo in 2003. His current research interests include behavioral finance and financial engineering. Kazuhiro Ueda, Ph.D.: He is an associate professor at the University of Tokyo. He received his B.A. degree from the Faculty of Liberal Arts and Science at the University of Tokyo in 1988. He also received M.A. and Ph.D. degrees in cognitive science from the Department of Systems Science at the University of Tokyo in 1990 and 1993. His current research interests include cognitive analysis on scientific problem solving, adaptive human-machine interface, artificial market and behavioral finance and cognitive robotics.     

Modeling and simulation of the market fluctuations by the finite range contact systems

In this paper, we model a new random stock price model for the stock markets based on the finite range contact process, which is a model for epidemic spreading that mimics the interplay of local infections and recovery of individuals, it is a member of a class of stochastic processes known as interacting particle systems. Then, we analyze the statistical behaviors of Shanghai Stock Exchange (SSE) Composite Index, Shenzhen Stock Exchange (SZSE) Composite Index, Dow Jones Industrial Average Index (DJIA), Nasdaq Composite Index (IXIC), the standard and Poor’s 500 Index (S&P500) and the simulative data derived from the finite range contact model by comparison. And six individual Chinese stocks from large-cap, mid-cap and small-cap categories are discussed. Furthermore, we investigate the long range correlations of the returns for these indices and the corresponding simulative data by applying the detrended fluctuation analysis. At last, the positive part of the probability distributions of the logarithmic returns for the actual data and the simulative data are studied by the q-Gaussian dynamic systems. The main objective of this work is to discuss the impact on the returns with the different range financial models.     

Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras,Brazil

Predicting the direction of stock price changes is an important factor, as it contributes to the development of effective strategies for stock exchange transactions and attracts much interest in incorporating variables historical series into the mathematical models or computer algorithms in order to produce estimations of expected price fluctuations. The purpose of this study is to build a neural model for the financial market, allowing predictions of stocks closing prices future behavior negotiated in BM&FBOVESPA in the short term, using the economic and financial theory, combining technical analysis, fundamental analysis and analysis of time series, to predict price behavior, addressing the percentage of correct predictions of price series direction (POCID or Prediction of Change in Direction). The aim of this work is to understand the information available in the financial market and identify the variables that drive stock prices. The methodology presented may be adapted to other companies and their stock. Petrobras stock PETR4, traded in BM&FBOVESPA, was used as a case study. As part of this effort, configurations with different window sizes were designed, and the best performance was achieved with a window size of 3, which the POCID index of correct direction predictions was 93.62% for the test set and 87.50% for a validation set.     

Enhanced stock price variation prediction via DOE and BPNN-based optimization

Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations.     

基于深度迁移学习的多尺度股票预测

股票市场不仅是上市公司的重要融资渠道,也是重要的投资市场,股票预测一直受到人们的关注。为了充分利用来自不同股票价格的信息,提高股票的预测效果,提出一种多尺度股票价格预测模型TL-EMD-LSTM-MA（TELM）。TELM模型通过经验模态分解将收盘价分解为多个时间尺度分量,不同时间尺度分量震荡频率不同,反映了不同的周期性信息;根据分量的震荡频率选择不同方法进行预测,高频分量利用深度迁移学习的方法训练堆叠LSTM,低频分量利用移动平均法进行预测;将所有分量的预测值相加作为收盘价的最终预测输出。通过深度迁移学习训练的堆叠LSTM,包含来自不同股票的信息,具备更多行业或市场的知识,能有效降低预测误差。利用移动平均法预测低频分量,更有效捕获股票的总体趋势。对中国A股市场内500支股票以及上证指数、深证成指等指数进行预测,结果表明,与其他模型相比,TELM预测误差最低,拟合优度最高。根据TELM预测的股票收盘价模拟股票交易过程,结果表明TELM投资风险低、收益高。     

The IPO window of opportunity for digital product and service firms

Recognizing the window of opportunity to go public for digital product and service (DPS) firms is especially critical because they have high fixed-to-variable cost (FCVC) ratios and winner-take-all industry competition. Too early and their very risky nature means a steep discount to their stock. Too late and they may not be able to sell stock or it may be too late to take advantage of a new product or service. We find that the size of the run-up in stock price at the IPO is higher (lower) for DPS firms the earlier (later) they go public and significantly more than for traditional firms. We also find that the DPS firms that go public earlier or later outside the window of opportunity are more likely to fail. This result is also stronger for DPS firms than for traditional firms.     

基于三次样条权函数神经网络的股价预测

随着经济的发展,股票投资已成为很多人的一种投资理财方式,而股票价格的预测也成为投资者关心和研究的焦点。建立一个运算速度和精确度都比较高的股价预测模型,对于金融投资者具有理论指导意义和实际应用价值。文中针对传统BP算法存在的学习速度慢、容易陷入局部极小值、隐层数不易确定等问题,使用三次样条权函数神经网络建立股价预测模型,克服了传统神经网络的缺点。仿真结果表明,该模型具有较高的预测精度,能够对股市进行有效的预测。     

基于Pytorch的LSTM模型对股价的分析与预测

股价预测一直都是股票投资者重点关注和重点研究的方向,针对股价具有高度非线性、高噪声、动态性等问题,提出一种基于自组织特征映射(SOM)神经网络和长短期记忆网络(LSTM)共同应用的股价预测方法。第一步聚类,使用python语言实现改进的自组织特征映射神经网络算法,将187支股票分成三类,三类股票以盈利能力大小进行聚类,并且求出每一类所包含的股票代码;第二步预测,基于Pytorch深度学习框架构造长短期记忆网络模型,分别对每一类中随机的3支股票进行股价预测,再通过均方误差和决定系数对预测结果进行评价。结果表明,在使用相同的预测模型对不同盈利能力的股票做股价预测时,盈利能力越大的股票,预测精度越高。此研究可以为投资者筛选出盈利能力更大的股票,并且在提高股价预测精度上也具有一定的贡献。     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.