The impact investment market has an estimated value of almost $1.6 trillion. Significant progress has been made in determining the financial returns of impact investing. Investors are still, however, in the early stages of determining impact return. In this study, the author proposes the use of impact internal rate of return (impact IRR) to evaluate and monitor impact investments. This approach, which utilizes components of modern portfolio theory, adapted financial tools, and existing datasets, is demonstrated herein through initial use cases and examples showing how it can be employed to optimize impact.
I identify a new signaling channel in ESG research by empirically examining whether environmental, social, and governance (ESG) investing remains valuable as large institutional investors increasingly shift toward artificial intelligence (AI). Using winsorized ESG scores of S&P 500 firms from Yahoo Finance and controlling for market value of equity, I conduct cross-sectional regressions to test the signaling mechanism. I demonstrate that Environmental, Social, Governance, and composite ESG scores strongly and positively signal higher debt-to-total-capital ratio, both individually and in various combinations. My findings contribute to the growing literature on ESG investing, offering economically meaningful signaling channel with implications for long-term portfolio management amid the rise of AI.
This study examines the performance of a volatility-based strategy using Chinese equity index ETF options. Initially successful, the strategy's effectiveness waned post-2018. By integrating GARCH models for volatility forecasting, the strategy's positions and exposures are dynamically adjusted. The results indicate that such an approach can enhance returns in volatile markets, suggesting potential for refined trading strategies in China's evolving derivatives landscape. The research underscores the importance of adaptive strategies in capturing market opportunities amidst changing trading dynamics.
Carlos G Escudero, Arianna Cortesi, Favio R Faifer
et al.
ABSTRACT NGC 4382 is a merger-remnant galaxy that has been classified as morphological type E2, S0, and even Sa. In this work, we performed a photometric and spectroscopic analysis of the globular cluster (GC) system of this peculiar galaxy in order to provide additional information about its history. We used a combination of photometric data in different filters, and multiobject and long-slit spectroscopic data obtained using the Gemini/GMOS instrument. The photometric analysis of the GC system, using the Gaussian Mixture Model algorithm in the colour plane, reveals a complex colour distribution within Rgal < 5 arcmin (26.1 kpc), showing four different groups: the typical blue and red subpopulations, a group with intermediate colours, and the fourth group towards even redder colours. From the spectroscopic analysis of 47 GCs, confirmed members of NGC 4382 based on radial velocities, we verified 3 of the 4 photometric groups from the analysis of their stellar populations using the ULySS code. NGC 4382 presents the classic blue (10.4 ± 2.8 Gyr, [Fe/H] = −1.48 ± 0.18 dex), and red (12.1 ± 2.3 Gyr, [Fe/H] = −0.64 ± 0.26 dex) GCs formed earlier in the lifetime of the galaxy, and a third group of young GCs (2.2 ± 0.9 Gyr; [Fe/H] = −0.05 ± 0.28 dex). Finally, analysis of long-slit data of the galaxy reveals a luminosity-weighted mean age for the stellar population of ∼2.7 Gyr, and an increasing metallicity from [Fe/H] = −0.1 to +0.2 dex in Rgal < 10 arcsec (0.87 kpc). These values, and other morphological signatures in the galaxy, are in good agreement with the younger group of GCs, indicating a common origin as a result of a recent merger.
Applying historical data from the USD LIBOR transition period, we estimate a joint model for SOFR, Fed Funds, and Eurodollar futures rates as well as spot USD LIBOR and term repo rates. The framework endogenously models basis spreads between each of the benchmark rates and allows for the decomposition of spreads. Modelling the LIBOR-OIS spread as credit and funding-liquidity roll-over risk, we find that the spike in the LIBOR-OIS spread during the onset of COVID-19 was mainly due to credit risk, while on average credit and funding-liquidity risk contribute equally to the spread.
In this report it is analyzed the focuses of the commercial dynamism of India, covering the fundamentals of growth rate, trade balance, coverage rate, openness rate, share of world indicators and then present each of them in detail.
We utilize the symmetric thermal optimal path (TOPS) method to examine the dynamic interaction patterns between the VIX and VIX futures markets. We document that the VIX dominates the VIX futures more in the first few years, especially before the introduction of VIX options. We further observe that the TOPS paths show an alternate lead-lag relationship instead of a dominance between the VIX and VIX futures in most of the time periods. Meanwhile, we find that the VIX futures have been increasingly more important in the price discovery since the launch of several VIX ETPs.
By analysing the restrictions that ensure the existence of capital market equilibrium, we show that the coefficient of relative risk aversion and the subjective discount factor cannot be high simultaneously as they are supposed to be to make the standard asset pricing consistent with financial stylised facts.
We define risk-free portfolios using three gauge invariant differential operators that require such portfolios to be insensitive to price changes, to be self-financing, and to produce a zero real return so there are no risk-free profits. This definition identifies the risk-free rate as the return of an infinitely diversified portfolio rather than as an arbitrary external parameter. The risk-free rate measures the rate of global price rescaling, which is a gauge symmetry of economies. We explore the properties of risk-free rates, rederive the Black Scholes equation with a new interpretation of the risk-free rate parameter as a that background gauge field, and discuss gauge invariant discounting of cash flows.
"What are the origins of risks?" and "How material are they?" -- these are the two most fundamental questions of any risk analysis. Quantitative Structuring -- a technology for building financial products -- provides economically meaningful answers for both of these questions. It does so by considering risk as an investment opportunity. The structure of the investment reveals the precise sources of risk and its expected performance measures materiality. We demonstrate these capabilities of Quantitative Structuring using a concrete practical example -- model risk in options on vol-targeted indices.
Stochastic dividend discount models (Hurley and Johnson, 1994 and 1998, Yao, 1997) present expressions for the expected value of stock prices when future dividends evolve according to some random scheme. In this paper we try to offer a more precise view on this issue proposing a closed-form formula for the variance of stock prices.
This work tried to detect the existence of a relationship between the graphic signals - or patterns - observed day by day in the Brazilian stock market and the trends which happen after these signals, within a period of 8 years, for a number of securities. The results obtained from this study show evidence of the existence of such a relationship, suggesting the validity of the Technical Analysis as an instrument to predict the trend of security prices in the Brazilian stock market within that period.
This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over-pessimism depends on the real chance of success and optimistic decision makers prefer receiving early information. In the portfolio choice problem, pessimistic investors tend to trade conservatively, however, they might trade aggressively if they are sophisticated enough to recognise the biases since low expectation can reduce their fear of loss.
We introduce the concept of spontaneous symmetry breaking to arbitrage modeling. In the model, the arbitrage strategy is considered as being in the symmetry breaking phase and the phase transition between arbitrage mode and no-arbitrage mode is triggered by a control parameter. We estimate the control parameter for momentum strategy with real historical data. The momentum strategy aided by symmetry breaking shows stronger performance and has a better risk measure than the naive momentum strategy in U.S. and South Korean markets.
The observation of power laws in the time to extrema of volatility, volume and intertrade times, from milliseconds to years, are shown to result straightforwardly from the selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks. The bias stems from the selection of price peaks that imposes a condition on the statistics of price change and of trade volumes that skew their distributions. For the intertrade times, the extrema and power laws results from the format of transaction data.
We extend the theory of asymmetric information in mispricing models for stocks following geometric Brownian motion to constant relative risk averse investors. Mispricing follows a continuous mean--reverting Ornstein--Uhlenbeck process. Optimal portfolios and maximum expected log--linear utilities from terminal wealth for informed and uninformed investors are derived. We obtain analogous but more general results which nests those of Guasoni (2006) as a special case of the relative risk aversion approaching one.
Alexander Alvarez, Sebastian Ferrando, Pablo Olivares
The paper studies the concepts of hedging and arbitrage in a non probabilistic framework. It provides conditions for non probabilistic arbitrage based on the topological structure of the trajectory space and makes connections with the usual notion of arbitrage. Several examples illustrate the non probabilistic arbitrage as well perfect replication of options under continuous and discontinuous trajectories, the results can then be applied in probabilistic models path by path. The approach is related to recent financial models that go beyond semimartingales, we remark on some of these connections and provide applications of our results to some of these models.
The economic crisis in Argentina around year 2002 provides a unique opportunity for Econophysics studies. The available data on individual income are analyzed to show that they correspond to non stationary states. However, the rather restricted size of the data survey imposes difficulties that must be overcome through a careful analysis, for a reliable use. A new method of data treatment is presented that could be helpful in theoretical studies.
This paper closely examines theoretical and practical aspects of the widely used discounted cash flows (DCF) valuation method. It assesses its potentials as well as several weaknesses. A special emphasize is being put on the valuation of companies using the DCF method. The paper finds that the discounted cash flow method is a powerful tool to analyze even complex situations. However, the DCF method is subject to massive assumption bias and even slight changes in the underlying assumptions of an analysis can drastically alter the valuation results. A practical example of these implications is given using a scenario analysis.