Applying Smart Beta in Alternative Markets

1. If you construct a value fund with small stocks that kicks out initial public offerings, bankruptcies and small companies that aren’t profitable, the screening of those duds is smart beta.

2. The typical S&P 500 index fund owns all 500 stocks in the index but doesn’t invest an equal amount in each. Traditional index fund weights are determined by market capitalization. However Smart-beta indexes tilt toward value stocks that perform well over time.

3. Smart-beta ETFs are rules-based. The rules are established in advance. Using the PowerShares S&P 500 ETF (SPLV) as an example, the fund takes the 100 least volatile stocks in the broader S&P 500 index, and then every three months it rebalances by selling what no longer fits its rules-based criteria and buys what does fit. 

Automotive Hubs in ASEAN

 As the automotive industry grapples with the fundamental changes in their business models. One thing remains constant: they will need to build the vehicles. They will need to build lots of them. They will need razor-sharp supply chains, economies of scale and supportive state structures. All of this indicates a strong likelihood that automotive ‘hubs’ will continue to play a major role in ASEAN's vehicle production.

Testing and Monitoring Risk Margin

1. A stochastic risk margin will be based on a stochastic model capable of predicting the probability distribution of the total outstanding claims, as the 75th percentile must be estimated.

2. The amount of comfort this gives us about the risk margin will depend on how much of the outstanding claims relates to payments to be made in the next transaction period and how confident we are about the payment pattern. 

3. Some of the more common stochastic models used for assessing risk margins are:

(i) Chain ladder bootstrap

(ii) Other non-parametric bootstraps based on different models

(iii) Mack’s model

(iv) Generalised linear models

(V) Adaptive generalised linear models

Practical Considerations for IBNR Issues

1. Excess or shock claims, especially their timing, number and amount, are  examples of real world disruptions to a health actuary’s IBNR calculations. There are other outside Influences on Health Claim Reserves and Patterns

2. Shock claims have a material impact on completion factors produced by development IBNR calculation methods. Often the adjudication time for these excess claims is longer; thus, when they are paid, they can lower all paid lag month’s completion factors, raising the overall claim reserve produced. 

3. By incorporating the excess claim’s impact (e.g., lower completion factors), one is essentially providing an ongoing reserve for a similarly expected excess claim. Alternatively, in the rare case that the large excess claim is paid much faster than other claims, the resulting completion factors will be increased, thus lowering reserves, a likely unwanted result.