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1.
《Information processing & management》2020,57(5):102212
Stock prediction via market data analysis is an attractive research topic. Both stock prices and news articles have been employed in the prediction processes. However, how to combine technical indicators from stock prices and news sentiments from textual news articles, and make the prediction model be able to learn sequential information within time series in an intelligent way, is still an unsolved problem. In this paper, we build up a stock prediction system and propose an approach that 1) represents numerical price data by technical indicators via technical analysis, and represents textual news articles by sentiment vectors via sentiment analysis, 2) setup a layered deep learning model to learn the sequential information within market snapshot series which is constructed by the technical indicators and news sentiments, 3) setup a fully connected neural network to make stock predictions. Experiments have been conducted on more than five years of Hong Kong Stock Exchange data using four different sentiment dictionaries, and results show that 1) the proposed approach outperforms the baselines in both validation and test sets using two different evaluation metrics, 2) models incorporating prices and news sentiments outperform models that only use either technical indicators or news sentiments, in both individual stock level and sector level, 3) among the four sentiment dictionaries, finance domain-specific sentiment dictionary (Loughran–McDonald Financial Dictionary) models the news sentiments better, which brings more prediction performance improvements than the other three dictionaries. 相似文献
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《Information processing & management》2023,60(3):103293
As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions. 相似文献
3.
上海股票市场有效性实证检验 总被引：28，自引：0，他引：28
本文以上海股票市场的指数和选取的一些股票的价格行为对象,用单位根和方差比方法检验其是否服从随机游走过程,从而判断其弱式市场有效性。结果发现除个别小公司股票价格行为不服从单位根过程外,指数和大多数股票价格的行为均显示出其具有一定程度的弱式市场有效性的特征。 相似文献
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股票价格中的公司流通股本规模信息——兼论国有股上市流通策略 总被引：4，自引：0，他引：4
本文以1997-2001年期间在沪深股票市场上市的595家A股公司为样本,实证分析股票市场对流通股本规模的反应。我们发现,股票价格与公司每股收益显著正相关,与流通股本规模显著负相关。股票价格与反映公司增长机会预期的行业市盈率在股票市场高涨时显著正相关,在股票市场低迷时相关性不显著。本文最后讨论了研究结果对国有股上市流通的政策意义。 相似文献
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本文以武汉市8个城区29个季度的住宅增量、存量销售价格数据为样本,运用面板数据Granger因果关系检验、随机效应变截距模型,研究了住宅增量市场与存量市场的价格关系。结果表明,在以住宅增量交易为主导的房地产市场中,增量住宅的价格对存量住宅的价格产生了显著的影响,增量住宅价格变量是存量住宅价格变量的Granger原因,反之则不成立;增量住宅价格每上升1％会带动存量住宅价格上升1．06％,增量住宅销售价格的上升,促进了存量住宅销售价格的快速上升。政府在严控房地产投机行为的同时,需要从中介制度、金融、税收等方面完善住宅交易制度,促进住宅增量市场向存量市场的转变。 相似文献
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通过获取在纳斯达克上市的34家中国互联网公司的股价和Alexa权威机构提供的流量指标,计算流量与股价的皮尔逊(Pearson)、肯德尔(Kendall)、斯皮尔曼(spearman)三种相关系数,并建立线性回归模型对上市网络公司流量指标与股价相关性进行实证分析,得出网络公司流量与股价存在相关性的结论。 相似文献
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镍是重要的战略性矿产。研究影响镍价格的各种因素及预测未来价格走势,对于更好地了解国际镍市场,并提出应对措施是非常必要的。本文在系统收集供需形势、供应安全、市场变化、价格走势等文献资料基础上,应用Eviews软件,以协整分析为主要手段,从供需角度入手,分析不锈钢产量（镍的主要消费领域）与LME镍年均价格、LME镍库存与价格、LME镍现货结算价与国内上海物贸镍均价的关系,并结合诸如印尼禁矿等多种突发、偶发的不确定因素,深入分析影响镍价格的主要因素,对镍价格未来走势进行初步分析。结果表明,需求因素（不锈钢产量）与价格具有长期互动关系,短期内关系不明显;库存短期内具有调节价格的作用,而长期内价格变化带动库存变化。国内价格受国际价格左右,我国对镍的定价依然缺乏话语权;镍价格受主要因素影响的同时,偶然、突发等不确定因素会对镍价格造成短时间冲击;镍价格在2015年下半年还会保持上升态势,在2~3年后,价格趋于合理,2025年左右,中国镍消费将达到顶点,此后,中国需求因素对镍价格的影响将削弱。 相似文献
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《Information processing & management》2023,60(4):103358
Stock forecasting has always been challenging as the stock market is affected by a combination of factors. Temporal Convolutional Network (TCN) based on convolutional structure has been widely used in time series prediction in recent years, but the dilated causal convolution structure leaves it unable to effectively learn the dependencies between data at different time points. This paper proposes a method for stock ranking prediction. To enhance the ability of TCN to handle dependencies within series, we first develop a channel-time dual attention module (CTAM). In conjunction with TCN to process complex historical stock price data, CTAM can adaptively learn the importance of multiple price nature series of stocks and model the dependencies between the data at different times. On the other hand, due to the market industry rotation, some stocks with specific industry attributes may become market preference for a period time. To apply the industry attributes to the stock prediction, we construct an industry-stock Pearson correlation matrix and extract a vector that fully characterizes the industry attributes of stocks from it through a matrix factorization algorithm. Furthermore, the historical market preference is modeled according to the industry attribute of the stocks to generate the dynamic correlation between stocks and market preference, and this correlation is combined with the historical price features extracted by TCN for stock ranking prediction. We conduct experiments on three datasets of 950 constituent stocks of the Shanghai Stock Exchange Index, 750 constituent stocks of the Shenzhen Stock Exchange 1000 Index and 486 stocks of the S&P500 to demonstrate the effectiveness of the proposed method. On the Shanghai Stock Exchange Index dataset, the Investment Return Ratio (IRR) obtained by using the predict results of our method to guide the exchange reached 1.416, and the Sharpe Ratio (SR) reached 2.346. On the Shenzhen Stock Exchange Index dataset, the IRR reached 1.434 and the Sharpe ratio reached 2.317. On the S&P500, the IRR reached 1.491 and the Sharpe ratio reached 2.031. 相似文献
9.
中国资本市场20多年的快速发展,跨越了西方发达国家上百年的历程,股价指数的研究与实践亦是如此。文章通过梳理这一演进过程中我国学者与业界取得的巨大进步,提出了我国未来股价指数研究与实践在统一编制方法、完善预测方法和平衡资本市场产品结构等方面存在的挑战,以期为这一领域的研究与发展提供可借鉴的思路。 相似文献
10.
运用ADF单位根检验、扩展的E-G检验和格兰杰因果检验等方法,对大连商品交易所的大豆、豆粕、玉米价格和饲料行业农牧类上市公司的代表——新希望集团股价进行了相关性实证研究。结果发现:大商所主要合约品种豆一、玉米期货价格与新希望股票价格之间存在长期均衡协整和双向格兰杰因果关系,而豆粕和新希望集团股票价格之间只存在协整关系,股票价格影响豆粕期货价格的单向格兰杰因果关系,豆一和玉米的价格发现功能要优于豆粕。 相似文献
11.
《Information processing & management》2020,57(4):102248
In information retrieval, the task of query performance prediction (QPP) is concerned with determining in advance the performance of a given query within the context of a retrieval model. QPP has an important role in ensuring proper handling of queries with varying levels of difficulty. Based on the extant literature, query specificity is an important indicator of query performance and is typically estimated using corpus-specific frequency-based specificity metrics However, such metrics do not consider term semantics and inter-term associations. Our work presented in this paper distinguishes itself by proposing a host of corpus-independent specificity metrics that are based on pre-trained neural embeddings and leverage geometric relations between terms in the embedding space in order to capture the semantics of terms and their interdependencies. Specifically, we propose three classes of specificity metrics based on pre-trained neural embeddings: neighborhood-based, graph-based, and cluster-based metrics. Through two extensive and complementary sets of experiments, we show that the proposed specificity metrics (1) are suitable specificity indicators, based on the gold standards derived from knowledge hierarchies (Wikipedia category hierarchy and DMOZ taxonomy), and (2) have better or competitive performance compared to the state of the art QPP metrics, based on both TREC ad hoc collections namely Robust’04, Gov2 and ClueWeb’09 and ANTIQUE question answering collection. The proposed graph-based specificity metrics, especially those that capture a larger number of inter-term associations, proved to be the most effective in both query specificity estimation and QPP. We have also publicly released two test collections (i.e. specificity gold standards) that we built from the Wikipedia and DMOZ knowledge hierarchies. 相似文献
12.
本文利用2008-2016年A股上市公司样本,从信息不对称角度探讨企业商誉对股价崩盘风险的影响。通过理论和实证分析,我们发现,企业商誉价值越高,企业所承担的股价崩盘风险越大;进一步研究发现,对于商誉计提减值准备的企业,商誉对企业股价崩盘风险的影响相对较弱;地区市场化水平越高,商誉对股价崩盘风险的影响越大。以上结果为商誉对资本市场股价崩盘风险的影响提供了经验证据,也为企业商誉价值管理提供了新的思路。 相似文献
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《Information processing & management》2023,60(2):103245
Natural language inference (NLI) is an increasingly important task of natural language processing, and the explainable NLI generates natural language explanations (NLEs) in addition to label prediction, to make NLI explainable and acceptable. However, NLEs generated by current models often present problems that disobey of commonsense or lack of informativeness. In this paper, we propose a knowledge enhanced explainable NLI framework (KxNLI) by leveraging Knowledge Graph (KG) to address these problems. The subgraphs from KG are constructed based on the concept set of the input sequence. Contextual embedding of input and the graph embedding of subgraphs, is used to guide the NLE generation by using a copy mechanism. Furthermore, the generated NLEs are used to augment the original data. Experimental results show that the performance of KxNLI can achieve state-of-the-art (SOTA) results on the SNLI dataset when the pretrained model is fine-tuned on the augmented data. Besides, the proposed mechanism of knowledge enhancement and rationales utilization can achieve ideal performance on vanilla seq2seq model, and obtain better transfer ability when transferred to the MultiNLI dataset. In order to comprehensively evaluate generated NLEs, we design two metrics from the perspectives of the accuracy and informativeness, to measure the quality of NLEs, respectively. The results show that KxNLI can provide high quality NLEs while making accurate prediction. 相似文献
15.
基于创业板的相关数据,利用改进后的股票价格模型和股票收益率模型,检验资本化研发支出的价值相关性,并对不同种类资本化研发支出(自主研发无形资产和开发支出)对公司价值影响的差异性进行了深入分析。研究发现,资本化研发支出与股票价格和股票收益率不相关,费用化研发支出与股票价格和股票收益率显著负相关;自主研发无形资产与股票价格不相关但与股票收益率显著正相关,开发支出与股票价格和股票收益率不相关。研究结果表明创业板上市公司资本化的研发支出具有一定的价值相关性,但股票市场投资者对创业板的资本化研发支出反应不充分,并且这种疑虑主要是由于开发支出导致的。 相似文献
16.
Liana Ermakova Jean Valère Cossu Josiane Mothe 《Information processing & management》2019,56(5):1794-1814
The increasing volume of textual information on any topic requires its compression to allow humans to digest it. This implies detecting the most important information and condensing it. These challenges have led to new developments in the area of Natural Language Processing (NLP) and Information Retrieval (IR) such as narrative summarization and evaluation methodologies for narrative extraction. Despite some progress over recent years with several solutions for information extraction and text summarization, the problems of generating consistent narrative summaries and evaluating them are still unresolved. With regard to evaluation, manual assessment is expensive, subjective and not applicable in real time or to large collections. Moreover, it does not provide re-usable benchmarks. Nevertheless, commonly used metrics for summary evaluation still imply substantial human effort since they require a comparison of candidate summaries with a set of reference summaries. The contributions of this paper are three-fold. First, we provide a comprehensive overview of existing metrics for summary evaluation. We discuss several limitations of existing frameworks for summary evaluation. Second, we introduce an automatic framework for the evaluation of metrics that does not require any human annotation. Finally, we evaluate the existing assessment metrics on a Wikipedia data set and a collection of scientific articles using this framework. Our findings show that the majority of existing metrics based on vocabulary overlap are not suitable for assessment based on comparison with a full text and we discuss this outcome. 相似文献
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随着经济全球化与中国粮食市场开放程度的不断提高,国内外粮食价格的关联性日益增强,然而国际粮食价格的上涨和下跌对国内粮食价格的影响作用机制存在差异。本文基于时变概率马尔科夫区制转移（MS-TVTP）模型,实证分析在粮食市场的不同运行阶段下,中国的合成粮食价格以及小麦、大米、大豆、玉米四类粮食价格受到国际价格的非对称性影响及其差异性。研究发现国际粮食价格通过贸易途径对国内粮食价格的影响存在非对称性效应,国内粮食价格倾向于对国际粮食价格上涨时的波动产生过度反应,而对国际粮食价格下跌时的波动反应不足。大豆受到国际价格影响最为显著,其正向影响作用在国内大豆价格上涨阶段大于下跌阶段;小麦、大米、玉米受到的非对称性影响形式因贸易形势、自给率等决定的价格传递形式不同而呈现差异。大豆低自给率和较高外贸依存度要求政府需健全大豆市场的政策保障,合理利用国际大豆价格的影响,规避国际市场异常波动对国内市场价格的冲击。 相似文献
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农产品市场价格预测是研究的难点.本文采用蔬菜市场价格数据,分别建立了BP神经网络模型、基于遗传算法的神经网络模型、RBF神经网络模型,并在前三种模型基础上,建立了一种集成预测模型;用北京市批发市场2003-2007年的蔬菜价格训练模型,对2008-2009年的数据进行了预报,前三种模型预报结果的平均绝对误差分别为0.1... 相似文献
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