Innovation is not new, but has gained massively in importance in the recent years. The innovative capability of a company is an important component for the long-term and sustainable success.
Our methods are based on a quantitative input-output approach.In this approach, a three-stage procedure is used to determine the innovative efficiency or the operational efficiency of companies with regard to the relevant key figures. In this manner, a quantitative comparison of the company is possible, and only the top Innovators or Efficiency hampions with a high appreciation potential are identified. The various methods are the result of scientific research, and comprise purely quantitative, predictable criteria. They are constantly adapted in line with the latest scientific findings and according to market conditions, which sets them apart from previous investment methods.
ALPORA ICA identifies companies with strong innovative capacity
ALPORA ICA (Innovation Capability Analytics) is based on a long-only strategy, the goal of which is generating a market alpha of > 5% in combination with low market risk. Improved innovative capacity directly impacts the performance of a company or stock. This has been proven in a multitude of studies (see list by Julian below).
ALPORA ICA is a mathematical maximization process for calculating an efficient innovation frontier by analyzing the input and output paramaters relevant for innovation that allow objective and quantitative determination of the innovative force of individual companies. ALPORA ICA enables direct comparison of various industries based on their innovative capability. Our research and application results indicate that companies on the efficient innovation frontier generate a better alpha.
Studies about ALPORA ICA:
- Kauffeldt, J., Brecht L., Schallmo D., Welz K., Measuring Innovation Capability in German ICT- companies by using DEA-Models, 2012
- Gunday, G., Ulusoy, G., Kilic, K., Alpkan, L., Effects of innovation types on firm performance”, 2011, International Journal of Production Economics 133 (2), pp. 662-676
- De Clercq, D , Thongpapanl, N. , Dimov, D., The moderating role of organizational context on the relationship between innovation and firm performance, 2011, IEEE Transactions on Engineering Management, Volume 58, Issue 3, Pages 431-444
- Rosenbusch, N., Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs, 2011, Journal of Business Venturing, 26(4):441-457
- Mei, M., Review on the study of relationship between technology innovation ability and enterprise performance, 2011 International Conference on E-Business and E-Government, ICEE2011 – Proceedings, S.8230-8234, 2011
- Terziovski M., Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource-based view, 2010, Strategic Management Journal, Volume 31, Issue 8, pages 892–902
- Varis, M., Littunen, H., Types of innovation, sources of information and performance in entrepreneurial SMEs, 2010, European Journal of Innovation Management 13 (2), pp. 128-154
- Dharmadasa P., Organisational Learning, Innovation and Performance in Family-Controlled Manufacturing Small and Medium-Sized Enterprises (SMEs) in Australia, 2009
- Salomo S.; Talke K., Strecker N., Innovation Field Orientation and Its Effect on Innovativeness and Firm Performance, 2008, Journal of Product Innovation Management 25 (6), pp. 560-576
- Yonggui Wang; Shanji Yao; Zhu Sun; He Jia; Sch., Nanjing Univ., Nanjing “Meta-analysis of the relationship between product innovation and business performance, 2008, 4th IEEE International Conference on Management of Innovation and Technology, S.906-911
ALPORA OCA identifies companies with operational performance capability
ALPORA OCA (Operational Capability Analytics) is based on a long-only strategy, the goal of which is generating a market alpha of > 3% in combination with low market risk. Improved operational economic performance directly impacts the performance of a company or stock. This has been proven in a multitude of studies.
ALPORA OCA is a mathematical maximization process for calculating an efficient operations frontier by analyzing the input and output paramaters relevant for operations to allow the objective and quantitative determination of the economic performance of individual companies. ALPORA OCA enables direct comparison of various industries based on their operational implementation capacities. Our research and application results indicate that companies on the efficient operations frontier generate a better alpha.
Studies about ALPORA OCA:
- Bogetoft, Peter (2012): Performance Benchmarking: Measuring and Managing Performance (Management for Professionals): Springer.
- Bowlin, William F. (1998): Measuring Performance. An Introduction to Data Envelopment Analysis (DEA). In: The Journal of Cost Analysis 15 (2), S. 3–27. DOI: 10.1080/08823871.1998.10462318.
- Düzakın, Erkut; Düzakın, Hatice (2007): Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis. An application of 500 major industrial enterprises in Turkey. In: European Journal of Operational Research 182 (3), S. 1412–1432. DOI: 10.1016/j.ejor.2006.09.036.
- Feroz, E. H.; Kim, S.; Raab, R. L. (2003): Financial statement analysis. A data envelopment analysis approach. In: J Oper Res Soc 54 (1), S. 48–58. DOI: 10.1057/palgrave.jors.2601475.
- Hitchner, J. R. (2011): Financial Valuation: Applications and Models: WILEY. Online verfügbar unter https://books.google.de/books?id=cZmTUINZApcC.
- Kaplan, Robert; Norton, David (2005): The Balanced Scorecard: Measures That Drive Performance. In: Harvard Business Review 83 (7).
- Malhotra, Rashmi (2008): Financial Statement Analysis Using Data Envelopment Analysis. Northeast Business and Economics Association’s Annual Meeting. Brooklyn, New York, 2008.
- Meyer, Christian A. (2007): Working Capital und Unternehmenswert. Eine Analyse zum Management der Forderungen und Verbindlichkeiten aus Lieferungen und Leistungen. Aufl. Wiesbaden: Dt. Univ.-Verl. (Gabler Edition Wissenschaft).
- Sueyoshi, T oshiyuki; Goto, Mika (2010): Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms. A use of Data Envelopment Analysis with strong complementary slackness condition. In: European Journal of Operational Research 207 (3), S. 1742–1753. DOI: 10.1016/j.ejor.2010.07.024.
- Venkatraman, N.; Ramanujam, Vasudevan (1986): Measurement of Business Performance in Strategy Research: A Comparison of Approaches. In: The Academy of Management Review 11 (4), S. 801–814. Online verfügbar unter http://www.jstor.org/stable/258398.
MACHINE LEARNING ANALYTICS
ALPORA MLA calculates and classifies companies based on expectations of futurerises or falls in share prices
ALPORA MLA (Machine Learning Analytics) makes it possible to set up market-neutral long-short portfolios with very low market risk. Machine learning is already being successfully applied in various areas (medicine, autonomous vehicles, etc.), and ALPORA has transferred these methods to investment management with success.
ALPORA MLA combines big data analyses with machine learning algorithms. ALPORA MLA is used to determine a separating hyper level which distinguishes between good and bad companies based on a variety of company parameters.
Our research and application results indicate that companies situated above or below this hyper level generate the required market-neutral portfolio.
Studies about ALPORA MLA:
- Akbani, S. Kwek, und N. Japkowicz. Applying support vector machines to imba- lanced datasets. In Proceedings of the 15th European Conference on Machine Learning, 2004
- Batuwita und V. Palade. Class imbalanced learning methods for support vec- tor machines. Imbalanced Learning: Foundations, Algorithms, and Applications, 2013
- L. Cao und F. E. H. Tay. Financial forecasting using support vector machines. Neural Computing & Apllications, 2001.
- Chandwani und M. Saluja Singh. Stock direction forecasting techniques: An empirical study combining machine learning system with market indicators in the indian context. International Journal of Computer Applications, 2014.
- Emir, H. Dincer, und M. Timor. A stock selection model based on fundamental and technical analysis variables by using artificial neural networks and support vector machines. Review of Economivs & Finance, 2012.
- Huang, Y. Nakamori, und S.-Y. Wang. Forecasting stock market movement di- rection with support vector machines. Computers & Operations Research, 2005.
- Ince und T. B. Trafalis. Kernel principal component analysis and support vector machines for stock price prediction. 2004 IEEE International Joint Conference on Neural Networks, 2004.
- Jiang, L. Xu, H. Wang, und H. Wang. Influencing factors for predicting financial performance based on genetic algorithms. Systems Research and Behavioral Science, 2009.
- Kamley, S. Jaloree, und R. S. Thakur. Performance forecasting of share market using machine learning techniques: A review. International Journal of Electrical and Computer Enineering, 2016.
- E. H. Tay und L. Cao. Improved financial time series forecasting by combining support vector machines wirh self-organising feature map. Intelligent Data Analysis, 2001.