Monkeys vs Fund managers - An evaluation of alternative equity indices

Author(s):

Andrew Clare

 et al.

A research collaboration between Aon Hewitt and Cass Business School has shown that alternative weighted indices offer better investment strategies than those of the market capitalisation index. Indeed, a computer simulation of random stock-picking, likened to the decision making of a monkey, outperformed a traditional market capitalisation weighted index every time.

Updated: 15/11/2013
Comments:
Views: 55,556

Pensions and Growth: Smoothing in Pension Scheme Funding Valuations

In response to continuing deficits in UK defined pension benefit schemes since the start of the millennium, various parties have asked the UK Government to allow pensions schemes to calculate liabilities using a smoothed average of bond yields over several years. This paper explores the reasons for this request, and looks at multiple evidence of what impact, both beneficial and potentially risky, such a move could have.

Updated: 06/01/2015
Comments: 9
Views: 3,437

Non-parametric prediction of stock returns based on yearly data. The long term view.

Author(s):

Jens Nielsen

 et al.
Industry:
Banking

Is it possible to predict equity returns and premiums with the use of empirical models? This is one of the most frequently pondered and studied questions in finance. In this research we examine the predictability of returns, taking the actuarial long term view and basing predictions on yearly data.

Updated: 25/06/2013
Comments:
Views: 3,369

Why managing a successful pension scheme is a bit like managing a successful football team.

What are the similarities between a defined benefit pension plan and a football team? On the face of it there may not seem to be many. After all, the football world is populated by overpaid, badly behaved, play acting prima donnas. A far cry from the sober and serious world of defined benefit pensions, where trustees devote huge amounts of their time in the interests of others, for little of no financial reward. However, Professor Andrew Clare draws some interesting analogies between the two.

Updated: 05/12/2012
Comments:
Views: 2,884

Optimal customer selection for cross-selling of financial services products

Author(s):

Jens Nielsen

 et al.

In this research a new methodology for optimal customer selection in cross-selling of financial services products, such as mortgage loans and non life insurance contracts, is presented. Financial services companies tend to possess significant databases and a long relationship with each customer. In this situation the challenge becomes to use the database in general and specific knowledge of the individual target to estimate the probability of a cross-sale, the cost of a cross-sale attempt, the average discounted future profit and the uncertainty of the profit of the entire cross-sale attempt for that individual. Once reliable estimates for the stochastics of the cross-sale process have been established, one can optimise the cross-sale profit according to a variety of criteria including return and risk. In this paper, we first consider the simple question of optimising the average profit, but we also consider one version of adjusting for risk when optimising cross-sale profits. Our extensive case study is taken from non-life insurance, where our sales probability model is provided to us by the company that also provided us with the data.

Updated: 29/10/2012
Comments:
Views: 4,276

Optimal customer selection for cross-selling of financial services products

Author(s):

Jens Nielsen

 et al.

In this research a new methodology for optimal customer selection in cross-selling of financial services products, such as mortgage loans and non life insurance contracts, is presented. Financial services companies tend to possess significant databases and a long relationship with each customer. In this situation the challenge becomes to use the database in general and specific knowledge of the individual target to estimate the probability of a cross-sale, the cost of a cross-sale attempt, the average discounted future profit and the uncertainty of the profit of the entire cross-sale attempt for that individual. Once reliable estimates for the stochastics of the cross-sale process have been established, one can optimise the cross-sale profit according to a variety of criteria including return and risk. In this paper, we first consider the simple question of optimising the average profit, but we also consider one version of adjusting for risk when optimising cross-sale profits. Our extensive case study is taken from non-life insurance, where our sales probability model is provided to us by the company that also provided us with the data.

Updated: 29/10/2012
Comments:
Views: 4,276

How financial services companies can use existing customer data to identify cross-selling opportunities.

Author(s):

Jens Nielsen

 et al.

Financial services companies wishing to increase their sales may look to their existing customer base for cross-selling opportunities. Information on customer behaviour can be analysed to assess whether or not more products should be offered. In particular, data on past claiming history and information on payment defaulting can be useful in determining how an individual customer is likely to act with another type of product. This study demonstrates a method for using historical information to both identify potential customers for cross-selling and assess their 'risk profile'. It may help companies improve their marketing to existing customers, and ultimately lead to higher profits.

Updated: 22/10/2012
Comments:
Views: 8,950

How financial services companies can use existing customer data to identify cross-selling opportunities.

Author(s):

Jens Nielsen

 et al.

Financial services companies wishing to increase their sales may look to their existing customer base for cross-selling opportunities. Information on customer behaviour can be analysed to assess whether or not more products should be offered. In particular, data on past claiming history and information on payment defaulting can be useful in determining how an individual customer is likely to act with another type of product. This study demonstrates a method for using historical information to both identify potential customers for cross-selling and assess their 'risk profile'. It may help companies improve their marketing to existing customers, and ultimately lead to higher profits.

Updated: 22/10/2012
Comments:
Views: 8,950

Adding Prior Knowledge to Quantitative Operational Risk Models

Author(s):

Jens Nielsen

 et al.

An analysis of the fundamental issues that arise in practice when modeling operational risk data. This paper addresses the statistical problem of estimating an operational risk distribution, both abundant data situations and when available data is challenged from the inclusion of external data or because of underreporting.

Updated: 11/10/2012
Comments:
Views: 3,850