pwc-archive/papers-with-abstracts huggingface dataset:
Effect of Short-Term Debt on Financial Growth of Non-Financial Firms Listed at Nairobi Securities Exchange
Authors: David Haritone Shikumo, Oluoch Oluoch, Joshua Matanda Wepukhulu
Date: 2020-11-05 00:00:00
Tasks: Descriptive, Management

A significant number of the non-financial firms listed at Nairobi Securities Exchange (NSE) have been experiencing declining financial performance which deter investors from investing in such firms. The lenders are also not willing to lend to such firms. As such, the firms struggle to raise funds for their operations. Prudent financing decisions can lead to financial growth of the firm. The purpose of this study is to assess the effect of short-term debt on financial growth of non-financial firms listed at Nairobi Securities Exchange for a period of ten years from 2008 to 2017. Financial firms were excluded because of their specific sector characteristics and stringent regulatory framework. The study is guided by Agency Theory and Theory of Growth of the Firm. Explanatory research design was adopted. The target population of the study comprised of 45 non-financial firms listed at the NSE for a period of ten years from 2008 to 2017. The study conducted both descriptive statistics analysis and panel data analysis. The result indicates that, short term debt explains 45.99% and 25.6% of variations in financial growth as measured by growth in earnings per share and growth in market capitalization respectively. Short term debt positively and significantly influences financial growth measured using both growth in earnings per share and growth in market capitalization. The study recommends that, the management of non-financial firms listed at Nairobi Securities Exchange to employ financing means that can improve the earnings per share, market capitalization and enhance the value of the firm for the benefit of its stakeholders.
Spontaneous symmetry breaking in Quantum Finance
Authors: Ivan Arraut, Alan Au, Alan Ching-biu Tse
Date: 2020-11-05 00:00:00

We analyze the phenomena of spontaneous symmetry breaking in Quantum Finance by using as a starting point the Black-Scholes (BS) and the Merton-Garman (MG) equations expressed in the Hamiltonian form. In this scenario the martingale condition (state) corresponds to the vacuum state which becomes degenerate when the symmetry of the system is spontaneously broken. We then analyze the broken symmetries of the system and we interpret from the perspective of Financial markets the possible appearance of the Nambu-Goldstone bosons.
Bear Markets and Recessions versus Bull Markets and Expansions
Authors: Abdulnasser Hatemi-J
Date: 2020-08-20 00:00:00

This paper examines the dynamic interaction between falling and rising markets for both the real and the financial sectors of the largest economy in the world using asymmetric causality tests. These tests require that each underlying variable in the model be transformed into partial sums of the positive and negative components. The positive components represent the rising markets and the negative components embody the falling markets. The sample period covers some part of the COVID19 pandemic. Since the data is non normal and the volatility is time varying, the bootstrap simulations with leverage adjustments are used in order to create reliable critical values when causality tests are conducted. The results of the asymmetric causality tests disclose that the bear markets are causing the recessions as well as the bull markets are causing the economic expansions. The causal effect of bull markets on economic expansions is higher compared to the causal effect of bear markets on economic recessions. In addition, it is found that economic expansions cause bull markets but recessions do not cause bear markets. Thus, the policies that remedy the falling financial markets can also help the economy when it is in a recession.
Strikingly Suspicious Overnight and Intraday Returns
Authors: Bruce Knuteson
Date: 2020-10-05 00:00:00

The world's stock markets display a strikingly suspicious pattern of overnight and intraday returns. Overnight returns to major stock market indices over the past few decades have been wildly positive, while intraday returns have been disturbingly negative. The cause of these astonishingly consistent return patterns is unknown. We highlight the features of these extraordinary patterns that have hindered the construction of any plausible innocuous explanation. We then use those same features to deduce the only plausible explanation so far advanced for these strikingly suspicious returns.
Use Cases of Quantum Optimization for Finance
Authors: Samuel Mugel, Enrique Lizaso, Roman Orus
Date: 2020-10-03 00:00:00
Tasks: Portfolio Optimization, Tensor Networks

In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.
Preventing crash in stock market: The role of economic policy uncertainty during COVID-19
Authors: Peng-Fei Dai, Xiong Xiong, Zhifeng Liu, Toan Luu Duc Huynh, Jianjun Sun
Date: 2020-10-02 00:00:00

This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the pandemic.
A Stock Prediction Model Based on DCNN
Authors: Qiao Zhou, Ningning Liu
Date: 2020-09-07 00:00:00
Tasks: Classification, General Classification, model, Prediction, Stock Prediction

The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research.
A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics
Authors: Danilo Vassallo, Giacomo Bormetti, Fabrizio Lillo
Date: 2019-10-03 00:00:00

We propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VAR(1) process. Our model encompasses alternative approaches available in literature and can be readily estimated by means of Kalman filtering and expectation maximization. This feature makes it convenient when the cross-sectional dimension of the portfolio increases. By applying the model to a portfolio of Dow Jones stocks, we find that the long term component co-integrates with the market principal factor, while the short term one captures transient swings of the market associated with the idiosyncratic components and captures the correlation structure of returns. Using quantile regressions, we assess the significance of the contemporaneous and lagged explanatory power of sentiment on returns finding strong statistical evidence when extreme returns, especially negative ones, are considered. Finally, the lagged relation is exploited in a portfolio allocation exercise.
Why Quantitative Structuring?
Authors: Andrei N. Soklakov
Date: 2015-07-26 00:00:00

Quality-designed consumer products are easy to recognize. Wouldn't it be great if the quality of financial products became just as apparent? This paper is addressed to financial practitioners. It provides an informal introduction to Quantitative Structuring -- a technology of manufacturing quality financial products (information derivatives). The presentation is arranged in three parts: the main text assumes no prior knowledge of the topic; important detailed discussions are arranged as a set of appendices; finally, a list of references provides further details including applications beyond product design: from model risk to economics and statistics.
Price, Volatility and the Second-Order Economic Theory
Authors: Victor Olkhov
Date: 2020-09-06 00:00:00

We introduce the price probability measure {\eta}(p;t) that defines the mean price p(1;t), mean square price p(2;t), price volatility {\sigma}p2(t)and all price n-th statistical moments p(n;t) as ratio of sums of n-th degree values C(n;t) and volumes U(n;t) of market trades aggregated during certain time interval {\Delta}. The definition of the mean price p(1;t) coincides with definition of the volume weighted average price (VWAP) introduced at least 30 years ago. We show that price volatility {\sigma}p2(t) forecasting requires modeling evolution of the sums of second-degree values C(2;t) and volumes U(2;t). We call this model as second-order economic theory. We use numerical continuous risk ratings as ground for risk assessment of economic agents and distribute agents by risk ratings as coordinates. We introduce continuous economic media approximation of squares of values and volumes of agents trades and their flows aggregated during time interval {\Delta}. We take into account expectations that govern agents trades and introduce aggregated expectations alike to aggregated trades. We derive equations for continuous economic media approximation on the second-degree trades. In the linear approximation we derive mean square price p(2;t) and volatility {\sigma}p2(t) disturbances as functions of the first and second-degree trades disturbances. Description of each next n-th price statistical moment p(n;t) with respect to the unit price measure {\eta}(p;t) depends on sums of n-th degree values C(n;t) and volumes U(n;t) of market trades and hence requires development of the corresponding n-th order economic theory.
The Inverted Parabola World of Classical Quantitative Finance: Non-Equilibrium and Non-Perturbative Finance Perspective
Authors: Igor Halperin
Date: 2020-08-09 00:00:00

Classical quantitative finance models such as the Geometric Brownian Motion or its later extensions such as local or stochastic volatility models do not make sense when seen from a physics-based perspective, as they are all equivalent to a negative mass oscillator with a noise. This paper presents an alternative formulation based on insights from physics.
Transparency versus Performance in Financial Markets: The Role of CSR Communications
Authors: Rajiv Kashyap, Mohamed Menisy, Peter Caiazzo, Jim Samuel
Date: 2020-08-08 00:00:00

Although companies are exhorted to provide more information to the financial community, it is evident that they choose different paths based upon their strategic emphasis and competitive environments. Our investigation explores the empirical boundary conditions under which firms choose to disclose versus withhold information from investors based upon their strategic emphasis. We found significant differences in terms of voluntary information disclosures between firms that consistently delivered positive earnings surprises versus those that delivered negative earnings surprises. We investigated this effect in a more granular fashion by separately examining differences in environmental, social, and governance disclosures between the two pools of firms. We found that in essence, the differences remained consistent and positive earnings firms were significantly more likely to disclose information about their ESG activities than their counterparts. Interestingly, none of the measures of financial performance were instrumental in distinguishing between the two pools of firms. However, our measures of reach -- as measured by the number of -- negative news stories lends credence to our findings. From a fund manager-s perspective, this finding should raise an immediate red flag firms that are likely to underperform are likely to be less transparent than overperformers.
Obamacare and a Fix for the IRS Iteration
Authors: Samuel J. Ferguson
Date: 2020-08-07 00:00:00

We model the quantities appearing in Internal Revenue Service (IRS) tax guidance for calculating the health insurance premium tax credit created by the Patient Protection and Affordable Care Act, also called Obamacare. We ask the question of whether there is a procedure, computable by hand, which can calculate the appropriate premium tax credit for any household with self-employment income. We motivate current IRS tax guidance, which has had self-employed taxpayers use a fixed point iteration to calculate their premium tax credits since 2014. Then, we give an example showing that the IRS iteration can lead to a divergent sequence of iterates. As a consequence, IRS guidance does not calculate appropriate premium tax credits for tax returns in certain income intervals, adversely affecting eligible beneficiaries. A bisection procedure for calculating premium tax credits is proposed. We prove that this procedure calculates appropriate premium tax credits for a model of simple tax returns. This is generalized to the case where premium tax credits are received in advance, which is the most common one in applications. We outline the problem of calculating appropriate premium tax credits for models of general tax returns. While the bisection procedure will work with the tax code in its current configuration, it could fail, eg, in states which have not expanded Medicaid, if a new deduction with certain properties were to arise.
Teaching Economics with Interactive Browser-Based Models
Authors: Juan Dominguez-Moran, Rouven Geismar
Date: 2020-08-06 00:00:00

Interactive simulation toolkits come in handy when teaching macroeconomic models by facilitating an easy understanding of underlying economic concepts and offering an intuitive approach to the models' comparative statics. Based on the example of the IS-LM model, this paper demonstrates innovative browser-based features well-suited for the shift in education to online platforms accelerated by COVID-19. The free and open-source code can be found alongside the standalone HTML files for the AD-AS and the Solow growth model at https://gitlab.tu-berlin.de/chair-of-macroeconomics/.
Towards a Sustainable Agricultural Credit Guarantee Scheme
Authors: Reason Lesego Machete
Date: 2020-07-22 00:00:00

Since 1986, Government of Botswana has been running an Agricultural Credit Guarantee Scheme for dry-land arable farming. The scheme purports to assist dry-land crop farmers who have taken loans with participating banks or lending institutions to help them meet their debt obligations in case of crop failure due to drought, floods, frost or hailstorm. Nonetheless, to date, the scheme has focused solely on drought. The scheme has placed an unsustainable financial burden on Government because it is not based on sound actuarial principles. This paper argues that the level of Government subsidies should take into account the gains made by farmers during non-drought years. It is an attempt to circumvent the challenges of correlated climate risks and recommends a quasi self-financing mechanism, assuming that the major driver of crop yield failure is drought. Moreover, it provides a novel subsidy and premium rate setting method.
Can Volatility Solve the Naive Portfolio Puzzle?
Authors: Michael Curran, Patrick O'Sullivan, Ryan Zalla
Date: 2020-05-07 00:00:00

We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of econometric and portfolio models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria.
Disturbing the Peace: Anatomy of the Hostile Takeover of China Vanke Co
Authors: Taurai Muvunza, Terrill Frantz
Date: 2020-03-10 00:00:00
Tasks: Anatomy

Wang Shi, a business mogul who created his empire of wealth from scratch, relished in his fame and basked in the glory of his affluent business. Nothing lasts forever! After mastering the turbulent business of real estate development in his country and therefore enjoying a rising and robust stock price, China Vanke Co. Ltd ("Vanke") founder and Chairman of the Board of Directors, Wang Shi was suddenly presented with a scathing notice from the Hong Kong Stock Exchange: rival Baoneng Group ("Baoneng") filed the regulatory documentation indicating that it had nicodemously acquired 5% of his company and was looking to buy more. Vanke case became brutal and sparked national controversy over corporate governance and the role of Chinese government in capital markets.
Machine Learning Portfolio Allocation
Authors: Michael Pinelis, David Ruppert
Date: 2020-03-02 00:00:00
Tasks: BIG-bench Machine Learning

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return with payout yields. The second is used to construct an optimized volatility estimate. Reward-risk timing with machine learning provides substantial improvements over the buy-and-hold in utility, risk-adjusted returns, and maximum drawdowns. This paper presents a new theoretical basis and unifying framework for machine learning applied to both return- and volatility-timing.
Interdependencies of female board member appointments
Authors: Matthias Raddant, Hiroshi Takahashi
Date: 2020-07-08 00:00:00

We investigate the networks of Japanese corporate boards and its influence on the appointments of female board members. We find that corporate boards with women show homophily with respect to gender. The corresponding firms often have above average profitability. We also find that new appointments of women are more likely at boards which observe female board members at other firms to which they are tied by either ownership relations or corporate board interlocks.
Measuring the Time-Varying Market Efficiency in the Prewar and Wartime Japanese Stock Market, 1924-1943
Authors: Kenichi Hirayama, Akihiko Noda
Date: 2019-11-11 00:00:00

This study explores the time-varying structure of market efficiency in the prewar and wartime Japanese stock market using a new market capitalization-weighted stock price index, the equity performance index. We examine whether the adaptive market hypothesis (AMH) is supported in that era. First, we find that the degree of market efficiency in the prewar and wartime Japanese stock market varies over time and with major historical events. This implies that the AMH is supported in this market. Second, we find that the variation in market efficiency observed in this study is significantly different from that in previous studies because of whether the price index is capitalization weighted. Finally, as government intervention in the market intensified throughout the 1930s, market efficiency declined as the war risk premium rose, especially from the time when the Pacific War became inevitable.
On the Evolution of Cryptocurrency Market Efficiency
Authors: Akihiko Noda
Date: 2019-04-20 00:00:00

This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) Bitcoin's market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.
Big Data links from Climate to Commodity Production Forecasts and Risk Management
Authors: Paulina Concha Larrauri, Upmanu Lall
Date: 2020-07-06 00:00:00
Tasks: Decision Making, Management

Frozen concentrated orange juice (FCOJ) is a commodity traded in the International Commodity Exchange. The FCOJ future price volatility is high because the world's orange production is concentrated in a few places, which results in extreme sensitivity to weather and disease. Most of the oranges produced in the United States are from Florida. The United States Department of Agriculture (USDA) issues orange production forecasts on the second week of each month from October to July. The October forecast in particular seems to affect FCOJ price volatility. We assess how a prediction of the directionality and magnitude of the error of the USDA October forecast could affect the decision making process of multiple FCOJ market participants, and if the "production uncertainty" of the forecast could be reduced by incorporating other climate variables. The models developed open up the opportunity to assess the application of the resulting probabilistic forecasts of the USDA production forecast error on the trading decisions of the different FCOJ stakeholders, and to perhaps consider the inclusion of climate predictors in the USDA forecast.