The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment

This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.

Deep Learning for seasonality modelling in Inflation-Indexed Swap pricing

An Inflation-Indexed Swap (IIS) is a derivative in which, at every payment date, the counterparties swap an inflation rate with a fixed rate. For the calculation of the Inflation Leg cash flows it is necessary to build a mathematical model suitable for the Consumer Price Index (CPI) projection. For this purpose, quants typically start by using market quotes for the Zero-Coupon swaps in order to derive the future trend of the inflation index, together with a seasonality model for capturing the typical periodical effects. In this study, we propose a forecasting model for inflation seasonality based on a Long Short Term Memory (LSTM) network: a deep learning methodology particularly useful for forecasting purposes. The CPI predictions are conducted using a FinTech paradigm, but in respect of the traditional quantitative finance theory developed in this research field. The paper is structured according to the following sections: the first two parts illustrate the pricing methodologies for the most popular IIS: the Zero Coupon Inflation-Indexed Swap (ZCIIS) and the Year-on-Year Inflation-Indexed Swap (YYIIS); section 3 deals with the traditional standard method for the forecast of CPI values (trend + seasonality), while section 4 describes the LSTM architecture, and section 5 focuses on CPI projections, also called inflation bootstrap. Then section 6 describes a robust check, implementing a traditional SARIMA model in order to improve the interpretation of the LSTM outputs; finally, section 7 concludes with a real market case, where the two methodologies are used for computing the fair-value for a YYIIS and the model risk is quantified.

Idiosyncratic volatility, investor sentiment, and returns of the GCC stock markets

Standard finance theory suggests that idiosyncratic volatility should not influence stock returns. In reality, if investors are unable to achieve efficient diversification, such risk may affect stock returns. The purpose of the study is to examine the presence of idiosyncratic volatility and sentiment in the stock markets of the GCC (Gulf Cooperation Council) countries. Monthly idiosyncratic volatility is estimated using the Fama-French three-factor model. A unified sentiment proxy for each market is created by employing Principal Component Analysis (PCA). Then, Ordinary Least Squares (OLS) regressions are applied. F-statistics, t-statistics, and adjusted R2s are used to test the presence of idiosyncratic volatility and sentiment in the GCC markets.Findings show that the effect of sentiment on stock returns is observed across all the GCC markets. Investor sentiment can weakly explain the effect of idiosyncratic volatility on stock returns. In general, investors do not price expected idiosyncratic volatility, and only the unexpected part of it affects stock returns. Overall, the first implication for investors is that they must consider market sentiment to predict the cross-section of stock prices and should not completely ignore the influence of idiosyncratic volatility on stocks. Secondly, the implication for policymakers is that they should motivate companies to go public so that investors have more options to diversify their portfolios across different sectors.

QF-TraderNet: Intraday Trading via Deep Reinforcement With Quantum Price Levels Based Profit-And-Loss Control

Reinforcement Learning (RL) based machine trading attracts a rich profusion of interest. However, in the existing research, RL in the day-trade task suffers from the noisy financial movement in the short time scale, difficulty in order settlement, and expensive action search in a continuous-value space. This paper introduced an end-to-end RL intraday trading agent, namely QF-TraderNet, based on the quantum finance theory (QFT) and deep reinforcement learning. We proposed a novel design for the intraday RL trader’s action space, inspired by the Quantum Price Levels (QPLs). Our action space design also brings the model a learnable profit-and-loss control strategy. QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of actions. The profitability and robustness of QF-TraderNet have been verified in multi-type financial datasets, including FOREX, metals, crude oil, and financial indices. The experimental results demonstrate that QF-TraderNet outperforms other baselines in terms of cumulative price returns and Sharpe Ratio, and the robustness in the acceidential market shift.

A Brand-New Method of Quantifying Lease Incremental Borrowing Rate under IFRS 16 and Topic 842

One of the most contentious issues of lessee’s accounting under IFRS 16 and FASB ASC Topic 842 has been how to compute a lessee’s incremental borrowing rate (hereafter, IBR). A proper quantification of IBR is important because it affects the amount of a lessee’s right-of-use asset and lease liability recognized at lease commencement in the statement of financial position, as well as depreciation and interest expenses ongoing. Such a determination poses theoretical and practical difficulties to companies. This article develops a brand-new method that follows a conceptual approach that converge accounting and finance theory, to strike a balance between rigorous theory and practical application for companies. The proposed approach starts with a lessee’s actual average borrowing rate and compares it with its theoretical average borrowing rate based on synthetic rating. It then flexes the average rate along the interest term curve and derives the monthly rates applicable to each monthly cash flow. It adjusts the rates based on each specific lease features as defined in the standards, periodically updates the specific lease interest rate curves, and computes a lease IBR as the internal rate of return of the cash flows discounted at the monthly specific rates applicable to that specific lease. It finally compares with benchmarks. The proposed model is innovative because it is framed within, and consistent with, the definition of incremental borrowing rate in those accounting pronouncements, uses three starting references cross-checking each other, includes both an internal perspective of a company’s actual interest rates and an external market perspective, and is relatively easy to model in a partially automated spreadsheet application.

Sustainability of the EU-28 Trade with China and the USA

There has been a lot of debate in global politics about fair trade, surpluses (also called positive trade accounts), and deficits (negative trade accounts) among the USA, China and the European Union (EU). The study aims to analyse the countries’ trade accounts through the lenses of international finance theory. Based on financial analytical models, the countries’ competitiveness and changes in their net foreign wealth were examined. The factors considered in the literature review are as follows: exchange rate, government tariff and tax policies, saving rate, manufacturing base, investments, natural resource abundance and others. The computation of the trade accounts was conducted using the ten-year international trade data (2009–2018) for the EU-28 member countries that became the main importer for China instead of the USA in 2019. The con ducted empirical research showed that Chinese trade has continuous deficits throughout European countries, and in some countries, it could be considered as an increasingly important structural issue (for example, in Poland and the Czech Republic). Trade with the USA, in turn, typically produces surplus for European countries, where Germany is the leader. The provided conclusions hold value for international trade managers in terms of their potential influence on public policy in the researched countries. In light of the financial crisis, the current export shock could be used by countries as an occasion to change the course and depart from the assumptions, which do not advocate for free trade.

STOCK PRICES: EFFECT OF BEHAVIORAL BIASES ON INVESTOR’S MINDSET IN GUJARAT STATE, INDIA

Stock market performances has been observed through a good deal of literature out of which it has been found out that the behavior of investors is affected through many parameters exist in the market like occurrence of any sudden event or influenceof Individual advice apart from the past behavior of the stock which is called rational information regarding stock performances as per the traditional finance theory. The main aim of this paper is to identify the mediating effect among various types of informationavailable in the market for investors to invest their money into the stock market as a part of different behavioral biases in the Gujarat State, India. Three types of constructs have been derived as a part of exploratory research design for this study byapplying exploratory factor analysis (EFA) which are Company History, IPO issues and Location benefit of the company out of the different variable taken for the study. Structural Equation modelling (SEM) techniques have been applied to check the mediatingeffect among these three constructs. The study has been concluded that Company information is an indirect construct, IPO issue is a dependent construct and Location of the company is a mediating construct which is revealing that there is a significant impact of Company information on the IPO issue done by any corporate in the market. IPO issue can also be affected by the Location of the company which has the mediating effect on it.

Cryptocurrencies and Finance Theories

Cryptocurrency (CCY) as a new key player in the currency system that has drawn the attention of scholars to examine its influence, relations and the opportunities that it may provide. However, a financial theoretical framework to connect CCY with financial theory is missing. This paper fills this gap by providing a review for the theoretical framework introduced in the literature to position CCY in investment and finance theories. This is done by studying the CCY literature and providing a critical feedback on the overall contributions in the area and possible venues for improvement. We report a need for a long-term analysis for CCY as this asset class is fairly new and sufficient data may not be available. Moreover, a better connection and linking with finance theories is required as it is significantly deficient. The promising potential of blockchain/ CCY stresses the need for interdisciplinary research including business, legal and information technology disciplines. In addition, the Covid-19 pandemic opens the door for further research to investigate the role of CCY as a hedge in the times of crises. Keywords: digital ledger technology, cryptocurrency bitcoin, finance theory, investment, fintech

Trading and Technology: An Information Systems Course Application

Financial markets today are highly computerized -- from software-driven order submission to price determination to straight-through clearing and settlement -- computer technology has displaced manual activities and streamlined functions throughout the trading value chain. The previous chapters examined microeconomic principles that underpin trading and price-setting, and finance theory that provides analytical frameworks for market outcomes. Our analysis introduces real market frictions and examines how transactions costs and heterogeneity among market participants makes market structure and tracing mechanism design crucial determinants of market outcomes and behavior. . In this chapter, we drill down further into the realities of a non-frictionless market in order to focus on how technology can enhance the efficiency of an actual marketplace. Challenging market design issues are encountered when developing and operating an actual trading facility, and as IT professionals know, the devil is in the details. The practical considerations in operating a market system successfully are the next topic this book addresses.