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on Financial Markets |
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Issue of 2021‒10‒18
seven papers chosen by |
| By: | Anish Rai; Ajit Mahata; Md. Nurujjaman; Sushovan Majhi; Kanish debnath |
| Abstract: | Recently, a stock price model is proposed by A. Mahata et al. [Physica A, 574, 126008 (2021)] to understand the effect of COVID-19 on stock market. It describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp crisis and prolong drop followed by quick recovery (U-shaped) or slow recovery for longer period (Swoosh-shaped recovery). We propose a modified model by introducing a new variable $\theta$ that quantifies the sentiment of the investors. $\theta=+1,~0,~-1$ for positive, neutral and negative sentiment, respectively. The model explains the movement of sectoral indices with positive $\phi$ showing U- and Swoosh-shaped recovery. The simulation using synthetic fund-flow ($\Psi_{st}$) with different shock lengths ($T_S$), $\phi$, negative sentiment period ($T_N$) and portion of fund-flow ($\lambda$) during recovery period show U- and Swoosh-shaped recovery. The results show that the recovery of the indices with positive $\phi$ becomes very weak with the extended $T_S$ and $T_N$. The stocks with higher $\phi$ and $\lambda$ recover quickly. The simulation of the Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. The simulation result is consistent with the real stock price movement. The time-scale ($\tau$) of the shock and recovery of these indices during the COVID-19 are consistent with the time duration of the change of negative sentiment from the onset of the COVID-19. This study may help the investors to plan their investment during different crises. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.03986&r= |
| By: | Ozili, Peterson K |
| Abstract: | Identifying the intersection between digital finance, green finance and social finance is important for promoting sustainable financial, social and environmental development. This paper suggests a link between digital finance, green finance and social finance. Using a simple conceptual model, I show that digital finance offers a smooth, efficient and seamless channel for individuals and corporations to fund social projects that deliver a social dividend, and green projects that promote a sustainable environment. The implication is that digital finance is both an enabler and a channel for efficient green financing and social financing. |
| Keywords: | green finance, social finance, digital finance, sustainable development, environment, sustainable finance, innovation |
| JEL: | G02 G20 G21 Q56 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110151&r= |
| By: | Yufei Wu; Mahmoud Mahfouz; Daniele Magazzeni; Manuela Veloso |
| Abstract: | The success of machine learning models is highly reliant on the quality and robustness of representations. The lack of attention on the robustness of representations may boost risks when using data-driven machine learning models for trading in the financial markets. In this paper, we focus on representations of the limit order book (LOB) data and discuss the opportunities and challenges of representing such data in an effective and robust manner. We analyse the issues associated with the commonly-used LOB representation for machine learning models from both theoretical and experimental perspectives. Based on this, we propose new LOB representation schemes to improve the performance and robustness of machine learning models and present a guideline for future research in this area. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.05479&r= |
| By: | Rapha\"el Semet; Thierry Roncalli; Lauren Stagnol |
| Abstract: | In this paper, we examine the materiality of ESG on country creditworthiness from a credit risk and fundamental analysis viewpoint. We first determine the ESG indicators that are most relevant when it comes to explaining the sovereign bond yield, after controlling the effects of traditional fundamental variables such as economic strength and credit rating. We also emphasize the major themes that are directly useful for investors when assessing the country risk premium. At the global level, we notice that these themes mainly belong to the E and G pillars. Those results confirm that extra-financial criteria are integrated into bond pricing. However, we also identify a clear difference between high-and middle-income countries. Indeed, whereas the S pillar is lagging for the highest income countries, it is nearly as important as the G pillar for the middle-income ones. Second, we determine which ESG metrics are indirectly valuable for assessing a country's solvency. More precisely, we attempt to infer credit rating solely from extra-financial criteria, that is the ESG indicators that are priced in by credit rating agencies. We find that there is no overlap between the set of indicators that predict credit ratings and those that directly explain sovereign bond yields. The results also highlight the importance of the G and S pillars when predicting credit ratings. The E pillar is lagging, suggesting that credit rating agencies are undermining the impact of climate change and environmental topics on country creditworthiness. This is consistent with the traditional view that social and governance issues are the main drivers of the sovereign risk, because they are more specific and less global than environmental issues. Finally, taking these different results together, this research shows that opposing extra-financial and fundamental analysis does not make a lot of sense. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.06617&r= |
| By: | Jaroslav Baran (ESM); Jan Voříšek (independent researcher) |
| Abstract: | Implied volatility and other forward-looking measures of option-implied uncertainty help investors carefully evaluate market sentiment and expectations. We construct several measures of implied uncertainty in European government bond futures. In the first part, we create new volatility indices, which reflect market pricing of subsequently realised volatility of underlying bond futures. We express volatility indices in both price and basis points, the latter being more intuitive to interpret; we document their empirical properties, and discuss their possible applications. In the second part, we fit the volatility smile using the SABR model, and recover option-implied probability distribution of possible outcomes of bond futures prices. We analyse shapes of the implied distribution, track its quantiles over time, calculate its skewness and kurtosis, and infer probabilities of a given upside or downside move in the price of bond futures or in the yield of their underlying CTD bond. We illustrate these complementary measures throughout the note using Bund futures as an example, and show the results for Schatz, Bobl, OAT, and BTP futures in the annex. Such forward-looking measures help market participants quantify the degree of future market uncertainty and thoroughly assess what risks are priced in. |
| Keywords: | bond futures, market expectations, options, probability density function, SABR, VIX, volatility index |
| JEL: | C13 G13 G14 G17 |
| Date: | 2020–05–13 |
| URL: | http://d.repec.org/n?u=RePEc:stm:wpaper:43&r= |
| By: | Mahdieh Yazdani |
| Abstract: | In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.07151&r= |
| By: | amri amamou, souhir; hellara, slaheddine |
| Abstract: | This paper aims to test the Credit default swaps (CDS) as vectors of contagion towards the bond market, classified by maturity, during the sovereign crisis for a sample of 10 developed Eurozone countries. By implementing an approach based on a VECM model subject to several econometric tests, this paper contributes to the literature by providing conclusions about the impact of a maturity effect on the vulnerability of a sovereign bond in the contagion facing the sovereign CDS market. Our findings suggest that the dynamic relationship between the CDS market and the public bond market is significantly related to the quality of the debt studied. |
| Keywords: | Sovereign CDS, sovereign bonds, contagion, spillover effects |
| JEL: | F3 G01 G13 |
| Date: | 2021–08–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109038&r= |