An Effective Clustering Approach to Stock Market Prediction

In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each sub-cluster belong to the same class. Then, for each sub-cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits

A longitudinal study of the textual characteristics in the chairman’s statements of Guinness - an impression management perspective

This is the Accepted Manuscript of the article published in Accounting, Auditing & Accountability Journal, 32(6), 1714-1741, available online: https://www.emerald.com/insight/content/doi/10.1108/AAAJ-01-2018-3308. Please cite the published version. This Accepted Manuscript is deposited under the CC BY-NC licence and that any reuse is allowed in accordance with the terms outlined by the licence.Purpose - This paper longitudinally analyses the evolution of multiple narrative textual characteristics in the chairman’s statements of Guinness from 1948 to 1996, with the aim of studying impression management influences. It attempts to contribute insights on impression management over time. Design/methodology/approach - The paper attempts to contribute to external accounting communication literature, by building on the socio-psychological tradition within the functionalist-behavioural transmission perspective. The paper analyses multiple textual characteristics (positive, negative, tentative, future and external references, length, numeric references and first person pronouns) over 49 years and their potential relationship to profitability. Other possible disclosure drivers are also controlled. Findings - The findings show that Guinness consistently used qualitative textual characteristics with a self-serving bias, but did not use those with a more quantitative character. Continual profits achieved by the company, and the high corporate/personal reputation of the company/chairpersons, inter alia, may well explain limited evidence of impression management associated with quantitative textual characteristics. The context appears related to the evolution of the broad communication pattern. Practical implications - Impression management is likely to be present in some form in corporate disclosures of most companies, not only those companies with losses. If successful, financial reporting quality may be undermined and capital misallocations may result. Companies with a high public exposure such as those with a high reputation or profitability may use impression management in a different way. Originality/value - Studies analysing multiple textual characteristics in corporate narratives tend to focus on different companies in a single year, or in two consecutive years. This study analyses multiple textual characteristics over many consecutive years. It also gives an original historical perspective, by studying how impression management relates to its context, as demonstrated by a unique data set. In addition, by using the same company, the possibility that different corporate characteristics between companies will affect results is removed. Moreover, Guinness, a well-known international company, was somewhat unique as it achieved continual profits. This is the Accepted Manuscript of the article published in Accounting, Auditing & Accountability Journal, 32(6), 1714-1741, available online: https://www.emerald.com/insight/content/doi/10.1108/AAAJ-01-2018-3308. Please cite the published version. This Accepted Manuscript is deposited under the CC BY-NC licence and that any reuse is allowed in accordance with the terms outlined by the licence

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Evaluating sentiment in financial news articles: Working paper series--11-10

We investigate the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% vs 50.4% without sentiment) and our simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively)

Are Managers 'Under-the-Weather' During Earnings Conference Calls?

Earnings conference calls represent an important communication channel between managers and investors. We examine the impact of weather-induced mood on manager behavior during these calls. Using a large sample of earnings conference calls from 2006 to 2017, we find managers speak more negatively and with less (more) quantitative information (uncertainty) when local weather conditions are bad. We further identify that this negative relation is less pronounced for CFOs than CEOs. Financial expertise mitigates negative behavior bias induced by weather and we confirm with subgroups of CEOs with previous financial experience. We document a significantly negatively market reaction to weather-induced behavior that cannot be explained by existing textual analysis methods. Our results remain significant after adding controls for investor mood, separating firms into those from big and small states, mediation tests, firm fixed effects, and propensity score matching. Taken together, our findings suggest that exogenous effects of bad weather significantly impact manager behavior that the market views negatively

NumHTML : numeric-oriented hierarchical transformer model for multi-task financial forecasting

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context

Essays on the U.S. GAAP-IFRS Convergence Project, the Nature of Accounting Standards, and Financial Reporting Quality

In this dissertation, I examine the changes to the nature of the accounting paradigms of U.S. GAAP and International Financial Reporting Standards (IFRS) over the course of the U.S. GAAP and IFRS convergence project. I further examine whether the changes to the nature of IFRS following convergence impacts the financial reporting quality. The motivation for this study is to provide an initial review of the progress of the convergence process between U.S. GAAP and IFRS that aims to converge both sets of standards towards more principles-based paradigms. The ultimate goal of the convergence process was the development of globally recognized high quality financial reporting standards (FASB, 2002) and the development of principles-based accounting standards was identified as an essential component of such a goal. Extant literature and professional practice agree that U.S. GAAP is more rules-based whereas IFRS is more principles-based. Thus, both the International Accounting Standards Board (IASB) and the U.S. Financial Accounting Standards Board (FASB) agreed that the convergence process would be an ideal vehicle to converge both sets of standards towards more principles-based paradigm. I document that over the course of the convergence project, the underlying accounting paradigm of U.S. GAAP has remained consistent whereas the accounting paradigm of IFRS has become more rules-based. Amendments to existing International Standards and newer standards added over the course of the convergence have moved IFRS towards a more rules-based nature which was not the intended outcome of the convergence process. I further examine if the changes in rules vs. principles-based nature of IFRS has impacted the accounting quality. Using a firm level instrument developed in Folsom et al. (2016) that measures the extent to which firms rely on principles-vs –rules-based accounting, standards I find a relation between firm reliance on principles-based standards and earnings persistence. I also find an association between firm reliance on principles-based standards and earnings ability to predict future cash flows as well as concurrent returns. More, importantly the results of my study provide initial evidence that these associations are significantly manifested in the post-convergence period