The Northern Front in Technological Cold War : Finland and the East-West Trade in the 1970s and 1980s

Peer reviewe

Book review: Suomen rahoitusmarkkinoiden murros 1980-luvulla: Oikeushistoriallinen tutkimus [The transformation of the Finnish financial markets in the 1980s: a study of legal history]

Kirja-arvio. Arvioitu teos: Suomen rahoitusmarkkinoiden murros 1980-luvulla: Oikeushistoriallinen tutkimus [The transformation of the Finnish financial markets in the 1980s: A study of legal history] by Markus Kari, Helsinki, Into Kustannus Oy, 2016, 563 pp., ISBN: 978-952-264-631-6.Non peer reviewe

Familjedynastier: så blev Sverige rikt [Family dynasties: How Sweden became rich] by Hans Sjögren [Book review]

Book review. Reviewed book: Familjedynastier: så blev Sverige rikt [Family dynasties: How Sweden became rich] by Hans Sjögren. Stockholm: Volante, 2017. 190 pp. ISBN 978-91-88123-92-3.Non peer reviewe

No room for neutrality? : The uncommitted European nations and the economic Cold War in the 1950s

When the United States and its leading Western European allies decided at the end of the 1940s to limit exports of strategic raw materials, products, and technology to communist countries, this policy came into conflict with the desire of many non-communist European countries not to take part in the East-West conflict. Many of these nations were also eager to trade with Eastern Europe. In this chapter, we explore the role of neutral countries in the context of Western embargo. Such an exercise was last completed in 1968, although many scholars have since looked at individual countries. We seek answers to a few interrelated key questions: How did the Western alliance, and in particular the United States, try to incorporate Austria, Switzerland, Sweden, Finland, and Ireland within the Western alliance’s export control system, usually known as the CoCom? 1 How did these neutral countries respond? How successful were the Americans and their allies in their efforts?Peer reviewe

Business, Economic Nationalism and Finnish Foreign Trade during the 19th and 20th centuries

In this article we look at a country, Finland, where economic nationalism had a major influence on government policy and on the development of business enterprises from the mid-19th century to the end of the millennium. During this time period, the Finns managed to combine export-oriented economic strategy with exceptionally nationalistic economic policies, even though they could not mould international trade regimes in a way that was beneficial for them.Yet, nationalism also had negative effects. Instead of limiting their activities on the protection of truly vital national interests, the Finnish authorities went on to create a highly restrictive system that placed severe obstacles in front of all foreigners who were willing to invest or work in Finland.Peer reviewe

Rikard Westerberg, Socialists at the gate: Swedish business and the defense of free enterprise, 1940–1985 (Stockholm: Stockholm School of Economics 2020). 352 pp. [Kirja-arvostelu]

PhD thesis reviewNon peer reviewe

Capitalism Under Attack : Economic Elites and Social Movements in Post-War Finland

For Finland, the post-war era began in September 1944 when it switched sides in the Second World War. The country, which had fought alongside Germany against the Soviet Union from the summer of 1941 onwards, was now left within the Soviet sphere of influence. In this altered political situation, new social movements, in particular those led by communists and other leftist activists, challenged the existing economic and political order. However, this article argues that the traditional economic elites were remarkably successful in defending their interests. When the ‘years of danger’, as the period 1944–1948 has been called in Finland, ended, it remained a country with traditional Western-style parliamentary democracy and a capitalist economic system.Peer reviewe

Enumeration of derangements with descents in prescribed positions

We enumerate derangements with descents in prescribed positions. A generating function was given by Guo-Niu Han and Guoce Xin in 2007. We give a combinatorial proof of this result, and derive several explicit formulas. To this end, we consider fixed point $\lambda$-coloured permutations, which are easily enumerated. Several formulae regarding these numbers are given, as well as a generalisation of Euler's difference tables. We also prove that except in a trivial special case, if a permutation $\pi$ is chosen uniformly among all permutations on $n$ elements, the events that $\pi$ has descents in a set $S$ of positions, and that $\pi$ is a derangement, are positively correlated

Enhancing Fund Selection Using Supervised Machine Learning : Evidence From the Nordic Mutual Fund Market

In this research we aim to extend the literature on the performance predictability in actively managed mutual funds. We use the Nordic mutual fund market as our laboratory. We develop a performance-enhancing system to assist retail investors in selecting mutual funds by utilizing gradient boosting, random forest, and deep neural networks. Furthermore, we seek to obtain positive abnormal returns from our predicted quintile portfolios. We thus retrieve data free of survivorship bias for 2748 Nordic mutual funds from Morningstar Direct. First, we run the algorithms to test the possibility of classifying alphas. Secondly, we create a ranking system that categorizes funds based on predicted alpha, enabling us to separate the best from the worst-performing mutual funds. At last, we benchmark our findings against Morningstar’s acknowledged rating platform to examine whether our top quintile portfolios manage to outperform Morningstar’s top quintile portfolio. We find that our models can classify the sign of the alpha coefficient, whereas gradient boosting and random forest does this exceptionally well. Further, we manage to create a categorization system significantly outperforming both an equally weighted and asset weighted benchmark on risk-adjusted returns. Finally, our best performing portfolios generate risk-adjusted returns in excess of Morningstar, although only significantly for gradient boosting. Results are further robust to changes in risk-adjustment models for both equity funds and fixed income funds. The findings are consistent with the current machine learning literature and enable us to state that machine learning algorithms can be used to select successful mutual funds.nhhma