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on Financial Markets |
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Issue of 2020‒12‒14
five papers chosen by |
| By: | Murray, Gordon |
| Abstract: | This paper reflects on the policy formation process in the burgeoning area of government’s involvement venture capital finance (VC) over the two decades 2000-2020. It looks at both why and how government VC funds (GVC) have evolved. The increasingly common vehicle of ‘hybrid’ co-investment funds, which include both public and private VC investors managed by a jointly approved private fund manager, is analysed. The evolution and greater refinement of public intervention in VC markets over time is acknowledged while noting that significant operational challenges remain. There is some evidence that later iterations of GVC programmes have started to add net value which may imply a public-policy learning process. A fluctuating supply over time for venture capital finance, particularly at the earliest stages of firm formation and growth, suggests the benefits of well-designed and complementary government venture capital activity. The rubric of Ten Meditations is employed as a device to communicate both problem and prescription across the academic/policy maker divide. The paper is intended to be relevant to policy makers while grounded in robust academic research. |
| Keywords: | Venture Capital, Entrepreneurial Finance, Government Policy, Co-investment Funds |
| JEL: | G2 G24 G28 |
| Date: | 2020–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104389&r=all |
| By: | Michael J. Fleming; Or Shachar; Peter Van Tassel |
| Abstract: | When the U.S. Treasury sells a new security, the security is announced to the public, auctioned a number of days later, and then issued sometime after that. When-issued (WI) trading refers to trading of the new security after the announcement but before issuance. Such trading promotes price discovery, which may reduce uncertainty at auction, potentially lowering government borrowing costs. Despite the importance of WI trading, and the advent of Treasury trading volume statistics from the Financial Industry Regulatory Authority (FINRA), little is known publicly about the level of WI activity. In this post, we address this gap by analyzing WI transactions recorded in FINRA’s Trade Reporting and Compliance Engine (TRACE) database. |
| Keywords: | Treasury market; trading volume; when issued; TRACE |
| JEL: | G1 |
| Date: | 2020–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:89094&r=all |
| By: | David Rappoport; Tugkan Tuzun |
| Abstract: | Market liquidity is expected to facilitate arbitrage, which in turn should affect the liquidity of the assets traded by arbitrageurs. We study this relationship using a unique dataset of equity and bond ETFs compiled from big trade-level data. We find that liquidity is an important determinant of the efficacy of the ETF arbitrage. For less liquid bond ETFs, Granger-causality tests and impulse responses suggest that this relationship is stronger and more persistent, and liquidity spillovers are observed from portfolio constituents to ETF shares. Our results inform the design of synthetic securities, especially when derived from less liquid instruments. |
| Keywords: | Exchange traded funds; ETF; Market liquidity; Law of one-price; Arbitrage; ETF premium |
| JEL: | G12 G14 |
| Date: | 2020–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2020-97&r=all |
| By: | Berardino Palazzo (Board of Governors of the Federal Reserve System); Ram Yamarthy (Office of Financial Research) |
| Abstract: | Using daily credit default swap (CDS) data going back to the early 2000s, we find a positive and significant relation between corporate credit risk and unexpected interest rate shocks around FOMC announcement days. Positive interest rate movements increase the expected loss component of CDS spreads as well as a risk premium component that captures compensation for default risk. Not all firms respond in the same manner. Consistent with recent evidence, we find that firm-level credit risk (as proxied by the CDS spread) is an important driver of the response to monetary policy shocks - both in credit and equity markets - and plays a more prominent role in determining monetary policy sensitivity than other common proxies of firm-level risk such as leverage and market size. A stylized corporate model of monetary policy, firm investment, and financing decisions rationalizes our findings. |
| Keywords: | credit risk, CDS, monetary policy, shock transmission, equity returns |
| Date: | 2020–12–03 |
| URL: | http://d.repec.org/n?u=RePEc:ofr:wpaper:20-05&r=all |
| By: | Xiao-Yang Liu; Hongyang Yang; Qian Chen; Runjia Zhang; Liuqing Yang; Bowen Xiao; Christina Dan Wang |
| Abstract: | As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL- Library. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09607&r=all |