nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒05‒02
eleven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. (Don't Fear) The Yield Curve, Reprise By Eric C. Engstrom; Steven A. Sharpe
  2. Hedge fund alpha and beta corrected for stale pricing By Godwin, Alexander
  3. Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization By Mufhumudzi Muthivhi; Terence L. van Zyl
  4. Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model By Jaydip Sen; Sidra Mehtab; Abhishek Dutta; Saikat Mondal
  5. Stock Return Predictability before the First World War By Rebecca Stuart
  6. A reverse Expected Shortfall optimization formula By Yuanying Guan; Zhanyi Jiao; Ruodu Wang
  7. The impact of ETF index inclusion on stock prices By Duffy, John; Friedman, Dan; Rabanal, Jean Paul; Rud, Olga
  8. Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets By Narayana Darapaneni; Anwesh Reddy Paduri; Himank Sharma; Milind Manjrekar; Nutan Hindlekar; Pranali Bhagat; Usha Aiyer; Yogesh Agarwal
  9. How Money Relates to Value? An Empirical Examination on Gold, Silver and Bitcoin By José Alves; João Quental Gonçalves
  10. Predicting the Bubble of Non-Fungible Tokens (NFTs): An Empirical Investigation By Kensuke Ito; Kyohei Shibano; Gento Mogi
  11. Stock Prices and the Russia-Ukraine War: Sanctions, Energy and ESG By Ming Deng; Markus Leippold; Alexander F. Wagner; Qian Wang

  1. By: Eric C. Engstrom; Steven A. Sharpe
    Abstract: In recent months, financial market perceptions about the future path of short-term interest rates have evolved amidst signals from policymakers suggesting that reduced monetary policy accommodation is in the offing. As with previous episodes of policy tightening, most recently in 2018, one can hear an attendant rise in the volume of commentary about a decline in the slope of the yield curve and the risk of "inversion," whereby long-term yields fall below shorter-maturity yields.
    Date: 2022–03–25
  2. By: Godwin, Alexander
    Abstract: This paper introduces a novel method for estimating the alpha and beta of hedge fund indices that corrects for stale pricing in reported returns. This approach can be further used to estimate volatility and other risk measures. We apply this technique to a composite hedge fund index and six strategy indices provided by HFR. Once corrected for stale pricing, we find these indices exhibit higher betas and volatility with negative or statistically insignificant positive alpha.
    Keywords: hedge funds; alternative investments; stale pricing; risk; beta; alpha; asset allocation; volatility
    JEL: G10 G11 G12
    Date: 2022–03–18
  3. By: Mufhumudzi Muthivhi; Terence L. van Zyl
    Abstract: The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.
    Date: 2022–03
  4. By: Jaydip Sen; Sidra Mehtab; Abhishek Dutta; Saikat Mondal
    Abstract: Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1, 2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LSTM model.
    Date: 2022–03
  5. By: Rebecca Stuart
    Abstract: This paper studies the predictability of stock returns using monthly data on eight markets over the period 1876-1913. In contrast to much of the existing literature I find broad predictability across stock markets. Market interest rates and seasonal dummies generally have predictive power, and in almost all of series studied there is a statistically significant autoregressive component. These relationships appear to be stable over the sample period. Testing returns from multiple indices for the same market indicates that the compilation of the index does not systematically affect its predictability. Finally, the results are robust to the exclusion of extreme observations.
    Keywords: stock returns, interest rates, Gold Standard
    JEL: G1 N2
    Date: 2022–03
  6. By: Yuanying Guan; Zhanyi Jiao; Ruodu Wang
    Abstract: The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function. We establish a reverse ES optimization formula, which says that a mean excess function at any fixed threshold is the maximum of an ES curve minus a linear function. Despite being a simple result, this formula reveals elegant symmetries between the mean excess function and the ES curve, as well as their optimizers. The reverse ES optimization formula is closely related to the Fenchel-Legendre transforms, and our formulas are generalized from ES to optimized certainty equivalents, a popular class of convex risk measures. We analyze worst-case values of the mean excess function under two popular settings of model uncertainty to illustrate the usefulness of the reverse ES optimization formula, and this is further demonstrated with an application using insurance datasets.
    Date: 2022–03
  7. By: Duffy, John (University of California, Irvine); Friedman, Dan (University of California, Santa Cruz); Rabanal, Jean Paul (University of Stavanger); Rud, Olga (University of Stavanger)
    Abstract: A growing body of evidence suggests that assets included in market indexes such as the S&P 500 trade at a premium relative to other assets. In this paper we look for evidence of such an index inclusion premium in a carefully controlled laboratory experiment. Our environment involves three assets and an Exchange Traded Fund (ETF) index asset. We model Authorized Participants (APs) as bots that create and redeem ETF shares by scanning the order books of the underlying assets. In one treatment, all three assets are included in the ETF index asset. In a second treatment, one of the three assets is excluded from the ETF index and is replaced by a second unit of one of the included assets; the included and excluded assets have identical fundamental values enabling a clean test of whether or not there exists an index inclusion premium. We consider a further variant of the excluded asset treatment where short-selling is allowed. We find that: (i) inclusion of an asset in the ETF index results in a substantial index premium, (ii) this result is tied to an order imbalance which arises when an identical asset is excluded from the index, and (iii) the premium and order imbalance persist even if short-selling is allowed.
    Keywords: Index Inclusion; ETF; Experimental Finance
    JEL: G10 G20
    Date: 2022–03–22
  8. By: Narayana Darapaneni; Anwesh Reddy Paduri; Himank Sharma; Milind Manjrekar; Nutan Hindlekar; Pranali Bhagat; Usha Aiyer; Yogesh Agarwal
    Abstract: Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.
    Date: 2022–04
  9. By: José Alves; João Quental Gonçalves
    Abstract: The present work offers a review on two divergent schools of thought regarding the subject of money and highlights why understanding it is important to grasp the workings and nature of the concept of money. We adopt a spontaneous order perspective on social institutions, considering money as one. Such framework allows for the construction of axioms from which we formulate our problem allowing us to ask how old forms of money such as Gold and Silver hold up in today’s world regarding their hedging properties. Moreover, we also do so for Bitcoin since we consider it an appropriate asset due to its specific characteristics and its (at the time of writing) more than 10-year life span. We resort to the Autoregressive Distributed Lag (ARDL) methodology in order to study our three assets in the context of the US dollar and the US Economy for two different time periods. We analyse price dynamics from 1980 to 2020 for gold and silver resorting to annual data. Regarding bitcoin we employ quarterly data from 2009 to 2020. We conclude that the theories that explain what money is, how it comes to be so and how certain types of “money assets” may serve both as an indirect hedge against inflation in the two interpretations of the word and as a “stock of value” have merits that might deserve further investigation. .
    Keywords: money, inflation, gold, silver, bitcoin
    JEL: B25 D46 E42 E51
    Date: 2022
  10. By: Kensuke Ito; Kyohei Shibano; Gento Mogi
    Abstract: Our study empirically predicts the bubble of Non-Fungible Tokens (NFTs): transferrable and unique digital assets on public blockchains. This subject is important because, despite their strong market growth in 2021, NFTs have not been studied in terms of bubble prediction. To achieve the purpose, we applied Logarithmic Periodic Power Law (LPPL) model to the time-series price data of major NFT projects, retrieved from Results implied that, as of December 20, 2021, (i) NFTs in general are in a small bubble (predicting price decline), (ii) Decentraland project is in a medium bubble (predicting price decline), and (iii) Ethereum Name Service and ArtBlocks projects are in a small negative bubble (predicting price increase). Future works are to refine the prediction by considering heterogeneity of NFTs, comparing other methods, and using more enriched data.
    Date: 2022–03
  11. By: Ming Deng (University of Zurich - Department of Banking and Finance); Markus Leippold (University of Zurich; Swiss Finance Institute); Alexander F. Wagner (University of Zurich - Department of Banking and Finance; Centre for Economic Policy Research (CEPR); European Corporate Governance Institute (ECGI); Swiss Finance Institute); Qian Wang (University of Zurich - Department of Banking and Finance; Inovest Partners AG)
    Abstract: An extraordinary mix of factors affected firm values in early 2022. In the build-up to and in the weeks after the Russian invasion of Ukraine, stocks strongly exposed to the regulatory risks of the transition to a low-carbon economy did well. This was true especially of US stocks. However, in Europe, these stocks tended to underperform after the invasion, arguably because of stronger expected policy responses supporting renewable energy sources in the face of the pronounced dependence of Europe on Russian oil and gas. Investors thus expect the speed of transition to a low-carbon economy to be diverging between the US and Europe. Relating six different Environmental, Social, and Governance (ESG) ratings with stock price performance yields mixed results, suggesting that investors cannot blindly rely on such ratings in general to indicate corporate resilience against crises. Companies which more frequently refer to inflation in their conference calls with analysts performed worse than their peers. Internationally oriented firms did poorly, and investors were particularly concerned regarding companies' exposure to China. Overall, the results offer a preview of the future economic impact of the Russia-Ukraine war.
    Keywords: Climate transition risk, energy, ESG, event study, inflation, resilience, Russia-Ukraine war, stock returns
    JEL: E3 G14 G01 Q54
    Date: 2022–04

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