Alacrity: a new model for venture acceleration

As research on venture accelerators develops, different models have emerged in the literature. These focus on the goals of the accelerator, which range from creating profit for managers and building support for business platforms to promoting regional economic development, as well as on its organizational form based on its for-profit or non-profit status. This article examines a novel model, the networked venture builder model, which offers an alternative perspective on the acceleration process. Using the example of the Alacrity Global Ecosystem (AGE), this article explores how the venture builder model includes characteristics of multiple accelerator types, which has helped it both rapidly grow new ventures and achieve substantial economic development goals. Synergies between the different aspects of the AGE’s organizational design help it support multiple missions. Drawing on interviews with key stakeholders and entrepreneurs within the AGE, this article describes the history of the AGE and its present form, providing new insights into a novel, but increasingly common, accelerator design and laying the basis for further research on its emerging organizational form.

     

The impact of capital goods prices on Africa's economic performance

Over the last four decades, the relative price of investment goods in Africa has gone through a relatively large decrease, resulting in a steady convergence towards the levels recorded in high-income countries. This fact begs the following question: To what extent might the relative price decrease be a driving force behind the economic performance of this continent? The paper addresses this question from the perspective of a panel ARDL approach, using the Solow growth model—augmented with barriers to investment—as a framework. The results reveal that a one-unit decrease in the relative price of investment leads, in the long term, to a 4% increase in per capita GDP, an increase that could be neutralised by a 6.5 percentage points decrease in the savings rate. The findings contribute to the case for a policy mix that combines policies geared towards reducing investment distortions with those promoting savings mobilisation.     

Recent advances in reinforcement learning in finance

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value- and policy-based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. We then discuss in detail the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. Our survey concludes by pointing out a few possible future directions for research.