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on Financial Markets |
| By: | Lee, King Fuei |
| Abstract: | The Rule of 40 is a popular financial guideline used by software-as-a-service (SaaS) industry participants to assess the operational health of the companies. This paper investigates the effectiveness of the Rule of 40 as a stock selection criterion. Our study analyses a sample of 1771 SaaS companies worldwide spanning the period 2003-2022. The findings demonstrate that the Rule of 40 adds value and delivers a moderately high Sharpe ratio as a stock selection tool. A modified rule, the SaaS Investing Rule of 65, is proposed and found to outperform the Rule of 40 in identifying relative winners and losers within the SaaS space. The effectiveness of the rules raises practical implications for investors and analysts. Additionally, we explore the effectiveness of alternative versions of the Rule of 40 using different measures of profitability, as well investigate whether the returns are driven by traditional style factors. |
| Keywords: | Rule of 40, SaaS, software-as-a-service, stock selection, SaaS Investing Rule of 65 |
| JEL: | G10 G12 |
| Date: | 2024–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121568 |
| By: | Jason Allen; Ali Hortaçsu; Eric Richert; Milena Wittwer |
| Abstract: | Many financial markets are populated by dealers, who commit to participate regularly in the market, and non-dealers, who do not commit. This market structure introduces a trade-off between competition and volatility, which we study using data on Canadian treasury auctions. We document a consistent exit trend by dealers and increasing, but irregular, participation by non-dealer hedge funds. Using a structural model, we evaluate the impact of dealer exit on hedge fund participation and its consequences for market competition and volatility. We find that hedge fund entry was partially driven by dealer exit, and that gains thanks to stronger competition associated with hedge fund entry are offset by losses due to the irregular market participation of hedge funds. We propose an issuance policy that stabilizes hedge fund participation at a sufficiently high average level and achieves revenue gains. |
| Keywords: | Debt management; Financial markets; Financial institutions; Market structure and pricing |
| JEL: | D44 D47 G12 G28 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:24-29 |
| By: | Maciej Wysocki (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning) |
| Abstract: | This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that system atic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday heding in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile. |
| Keywords: | S&P500 Index options, Option Pricing Models, Black-Scholes-Merton model, Variance-Gamma model, Implied Volatility, Volatility Risk Premium, Volatility Spreads, Dynamic Hedging |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2024-14 |
| By: | Ozili, Peterson K |
| Abstract: | The purpose of this article is to explore the role of artificial intelligence, or AI, in a central bank digital currency project and its challenges. Artificial intelligence is transforming the digital finance landscape. Central bank digital currency is also transforming the nature of central bank money. This study also suggests some considerations which central banks should be aware of when deploying artificial intelligence in their central bank digital currency project. The study concludes by acknowledging that artificial intelligence will continue to evolve, and its role in developing a sustainable CBDC will expand. While AI will be useful in many CBDC projects, ethical concerns will emerge about the use AI in a CBDC project. When such concerns arise, central banks should be prepared to have open discussions about how they are using, or intend to use, AI in their CBDC projects. |
| Keywords: | artificial intelligence, central bank digital currency, CBDC, machine learning, deep learning, cryptocurrency, CBDC project, CBDC pilot, blockchain |
| JEL: | E50 E51 E52 E58 O31 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121567 |
| By: | Falk Bräuning; Hillary Stein |
| Abstract: | Using confidential microdata, we show that shocks to primary dealers’ risk-bearing constraints have significant effects on the US Treasury securities market. In response to tighter constraints, dealers reduce their Treasury positions, triggering a reduction in aggregate turnover and an increase in bid–ask spreads. These effects are more pronounced in securities that contribute more to the utilization of risk constraints. The impaired intermediation also affects Treasury yields, amplifying the yield response to net demand shifts. Moreover, tighter dealer constraints weaken Treasury auction outcomes: Bid-to-cover ratios decline, driven by dealers’ less aggressive bidding, and the highest yield accepted by participants rises, thereby increasing the government’s cost of issuing debt. Using our estimates, we back out key elasticities to show that the shadow cost of dealer constraints is as high as one-third of dealers’ intermediation margin. |
| Keywords: | Treasury market; primary dealers; intermediation; risk constraints |
| JEL: | G10 G12 G18 G21 |
| Date: | 2024–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:98561 |
| By: | Francisco Peñaranda (Queens College CUNY); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | The purpose of this survey is to summarize the academic literature that studies some of the ways in which portfolio management has been affected in recent years by the availability of big datasets: many assets, many characteristics for each of them, many macro predictors, and various sources of unstructured data. Thus, we deliberately focus on applications rather than methods. We also include brief reviews of the financial theories underlying asset management, which provide the relevant background to assess the plethora of recent contributions to such an active research field. |
| Keywords: | Conditioning information, intertemporal portfolio decisions, machine learning, mean-variance analysis, stochastic discount factors. |
| JEL: | G11 G12 C55 G17 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:cmf:wpaper:wp2024_2411 |
| By: | Cristiano Arbex Valle |
| Abstract: | Portfolio optimisation is widely acknowledged for its significance in investment decision-making. Yet, existing methodologies face several limitations, among them converting optimal theoretical portfolios into real investment is not always straightforward. Several classes of exogenous (real-world) constraints have been proposed in literature with the intent of reducing the gap between theory and practice, which have worked to an extent. In this paper, we propose an optimisation-based framework which attempts to further reduce this gap. We have the explicit intention of producing portfolios that can be immediately converted into financial holdings. Our proposed framework is generic in the sense that it can be used in conjunction with any portfolio selection model, and consists of splitting the portfolio selection problem into two-stages. The main motivation behind this approach is in enabling automated investing with minimal human intervention, and thus the framework was built in such a way that real-world market features can be incorporated with relative ease. Among the novel contributions of this paper, this is the first work, to the best of our knowledge, to combine futures contracts and equities in a single framework, and also the first to consider borrowing costs in short positions. We present extensive computational results to illustrate the applicability of our approach and to evaluate its overall quality. Among these experiments, we observed that alternatives from literature are susceptible to numerical errors, whereas our approach effectively mitigates this issue. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.00887 |
| By: | Shubham Singh |
| Abstract: | The study examines whether broader market factors and the Fama-French three-factor model can effectively analyze the idiosyncratic risk and return characteristics of Bitcoin. By incorporating Fama-french factors, the explanatory power of these factors on Bitcoin's excess returns over various moving average periods is tested. The analysis aims to determine if equity market factors are significant in explaining and modeling systemic risk in Bitcoin. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.19401 |