nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒08‒29
six papers chosen by

  1. The Determinants of Bank Liquid Asset Holdings By Stulz, Rene M.; Taboada, Alvaro G.; Van Dijk, Mathijs A.
  2. Predicting the unpredictable: New experimental evidence on forecasting random walks By Te Bao; Brice Corgnet; Nobuyuki Hanaki; Yohanes E. Riyanto; Jiahua Zhu
  3. Autoencoding Conditional GAN for Portfolio Allocation Diversification By Jun Lu; Shao Yi
  4. Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning By Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali
  5. A note on VIX for postprocessing quantitative strategies By Jun Lu; Minhui Wu
  6. Green Factor Influence on the Yield of Stocks and Bonds in the Russian Financial Market By Yulia Vymyatnina; Aleksandr Chernykh

  1. By: Stulz, Rene M. (Ohio State University); Taboada, Alvaro G. (Mississippi State University); Van Dijk, Mathijs A. (Erasmus University Rotterdam)
    Abstract: Bank liquid asset holdings vary significantly across banks and through time. The determinants of liquid asset holdings from the corporate finance literature are not useful to predict banks’ liquid asset holdings. Banks have an investment motive to hold liquid assets, so that when their lending opportunities are better, they hold fewer liquid assets. We find strong support for the investment motive. Large banks hold much more liquid assets after the Global Financial Crisis (GFC), and this change cannot be explained using models of liquid asset holdings estimated before the GFC. We find evidence supportive of the hypothesis that the increase in liquid assets of large banks is due at least in part to the post-GFC regulatory changes.
    JEL: G21 G28
    Date: 2022–07
  2. By: Te Bao; Brice Corgnet; Nobuyuki Hanaki; Yohanes E. Riyanto; Jiahua Zhu
    Abstract: We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield & Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in the first task. We find that subjects also overreact less to the trend when there is less momentum in the stock price in the second task, though the momentum factor that is significant is the autocorrelation instead of the number of reversals per se. Our subjects also appear to use other variables such as amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.
    Date: 2022–07
  3. By: Jun Lu; Shao Yi
    Abstract: Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches.
    Date: 2022–06
  4. By: Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali
    Abstract: Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management.
    Date: 2022–07
  5. By: Jun Lu; Minhui Wu
    Abstract: In this note, we introduce how to use Volatility Index (VIX) for postprocessing quantitative strategies so as to increase the Sharpe ratio and reduce trading risks. The signal from this procedure is an indicator of trading or not on a daily basis. Finally, we analyze this procedure on SH510300 and SH510050 assets. The strategies are evaluated by measurements of Sharpe ratio, max drawdown, and Calmar ratio. However, there is always a risk of loss in trading. The results from the tests are just examples of how the method works; no claim is made on the suggestion of real market positions.
    Date: 2022–07
  6. By: Yulia Vymyatnina; Aleksandr Chernykh
    Abstract: In this paper we test whether environmental characteristics of assets influence their returns for the case of Russian financial market. Our main hypothesis based on the relevant literature is that if a spread between ``greenÕÕ and ``brownÕÕ assetsÕ yield exists, it should be in favour of the brown assets. We employ relevant econometric models separately for stocks and for bonds. For the stock market we used realized returns and estimated the role of the green factor in the yield using the three-factor Fama-French model. While the resulting coefficient was not significant, on the whole we have observed that the realized return of the climate-risk hedge portfolio had a negative value over a nearly ten-year observation period. We have also demonstrated the applicability of the green factor calculations for estimating the degree of climate risk exposure for individual companies. Using data on a number of green bonds and their chosen ``twinÕÕ bonds, we calculate the difference in the premium in the yield to maturity over that of a similar government bond for all pairs of ``twinÕÕ bonds and proceed to check if this difference is significant, and if it can be attributed to the Greenium factor. We find that over the stable period in Russian financial markets (allowing for the most stable results) ``greenÕÕ bonds have lower yield to maturity Ñ a result that is in line with previous results for other markets and suggests that green financing might be cheaper for companies. On the whole our results suggest that environmental considerations might be relevant in the Russian financial market during stable macroeconomic periods.
    Keywords: ESG, sustainable investing, Greenium, Russia
    JEL: G10 G12
    Date: 2022–08–08

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